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Physical activity (PA) and physical function (PF) are modifiable risk factors for falls in older adults, but their ability to predict future fall incidence is unclear. The purpose of this study was to determine the predictive ability of baseline measures of PA, PF, and lower limb strength on future falls. A total of 104 participants underwent baseline assessments of PA, PF, and lower limb strength. Falls were monitored prospectively for 12 months. Eighteen participants fell at least once during the 12-month follow-up. Participants recorded almost exclusively sedentary levels of activity. PA, PF, and lower limb strength did not differ between fallers and nonfallers. Twelve participants, who reported a minor musculoskeletal injury in the past 6 months, experienced a fall. The results of this study suggest that in a cohort of highly functioning, sedentary older adults, PA does not distinguish fallers from nonfallers and that the presence of a recent musculoskeletal injury appears to be a possible risk factor for falling. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the validity of the Physical Activity Questionnaire for Older Children (PAQ-C) to assess the moderate- to vigorous-intensity physical activity (MVPA) level of children and adolescents diagnosed with HIV and propose cut-points, with accelerometer measures as the reference method. Method: Children and adolescents, aged 8–14 years (mean age = 12.21 y, SD = 2.09), diagnosed with HIV by vertical transmission, participated in the study. MVPA was investigated through the PAQ-C and triaxial accelerometer (ActiGraph GT3X+). Receiver operating characteristic curve and sensitivity and specificity values were used to identify a cut-point for PAQ-C to distinguish participants meeting MVPA guidelines. Results: Fifty-six children and adolescents participated in the study. Among those, 16 met MVPA guidelines. The PAQ-C score was significantly related to accelerometry-derived MVPA (ρ =.506, P <.001). The PAQ-C score cut-point of 2.151 (sensitivity = 0.625, specificity = 0.875) was able to discriminate between those who met MVPA guidelines and those that did not (area under the curve = 0.751, 95% confidence interval, 0.616–0.886). Conclusion: The PAQ-C was useful to investigate MVPA among children and adolescents diagnosed with HIV and to identify those who meet MVPA guidelines. [ABSTRACT FROM AUTHOR]

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]

Assessing bone's response to physical activity interventions is challenging. This randomized controlled trial investigates if changes in bone turnover markers can offer an early evaluation of a physical activity intervention's effectiveness in improving bone mineral density (BMD) in premenopausal women. Participants in the intervention group (n = 27, with 24 completing the trial) were instructed to walk at least 10,000 steps every day on a brisk walk and to execute 60 jumps daily, each surpassing 4g of acceleration, using an accelerometer-based wearable device. Meanwhile, the control group (n = 26, with 18 completing the trial) continued with their usual lifestyle. Bone turnover markers, comprising of C-terminal telopeptide of Type I collagen, procollagen Type 1 N-terminal propeptide, and total osteocalcin (carboxylated and undercarboxylated) were measured at baseline and midway through the intervention (3 months). Dual-energy X-ray absorptiometry scans of the hip and lumbar spine were conducted at baseline and the end of the intervention (6 months) to estimate BMD. Analysis of covariance exhibited significant differences between groups in procollagen Type 1 N-terminal propeptide (−6.74 μg/L, p =.023) and C-terminal telopeptide of Type I collagen (−83 ng/L, p =.043) after 3 months, and in femoral neck BMD (+0.024 g/cm2, p =.016), total hip BMD (+0.036 g/cm2, p =.004), and lumbar spine BMD (+0.026 g/cm2, p =.020) after 6 months. A significant correlation (r = −.73; p <.001) was detected between reductions in C-terminal telopeptide of Type I collagen and increases in femoral neck BMD. In conclusion, this intervention improved BMD in premenopausal women, with bone turnover markers potentially useful for early intervention assessment, though further research is needed. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU's location has on running data, a reference IMU was 'correctly' placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was 'misplaced' 0.05 m away, simulating a 'worst-case' misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to -10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

The aim of this investigation was to quantify professional rugby union player activity profiles after the most intense (peak) passages of matches. Movement data were collected from 30 elite and 30 subelite professional rugby union athletes across respective competitive seasons. Accelerometer-derived PlayerLoad and global navigation satellite system–derived measures of mean speed and metabolic power were analyzed using a rolling-average method to identify the most intense 5- to 600-second passages (ie, worst-case scenarios) within matches. Player activity profiles immediately post their peak 5- to 600-second match intensity were identified using 5 epoch duration-matched intervals. Mean speed, metabolic power, and PlayerLoad declined sharply (∼29%–86%) after the most intense 5 to 600 seconds of matches. Following the most intense periods of rugby matches, exercise intensity declined below the average match-half intensity 81% of the time and seldom returned to or exceeded it, likely due to a host of individual physical and physiological characteristics, transient and/or accumulative fatigue, contextual factors, and pacing strategies. Typically, player exercise intensities after the most intense passages of matches were similar between match halves, positional groups, and levels of rugby competition. Accurate identification of the peak exercise intensities of matches and movement thereafter using novel methodologies has improved the limited understanding of professional rugby union player activity profiles following the worst-case scenarios of matches. Findings of the present study may inform match-representative training prescription, monitoring, and tactical match decisions (eg, substitutions and positional changes). [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the long-term effect of body composition, physical activity, and cardiorespiratory fitness (CRF) from puberty on arterial health in late adolescent males. Methods: The cumulative burden of physical activity (measured with accelerometer), CRF (VO2peak0.82), and body composition (body mass index, fat mass, and fat percentage) from puberty to late adolescence (sum of 4 time points from 12 to 18 y) was assessed in 102 males. Additional analysis on the first (T1) and last (T4) time points was performed. Intima-media thickness (IMT), carotid-femoral pulse wave velocity, and augmentation index adjusted to heart rate of 75 beats per minute (bpm) as dependent variables were measured at T4 and analyzed in multivariable regression models adjusted for known risk factors including maturation, blood pressure, and smoking habits. Results: T1 and cumulative body composition measures were independently associated with IMT, while cumulative (β = −0.011, P =.036) and T4 (β = −0.0.031, P =.001) CRF revealed independent associations with IMT. Individuals with moderate to vigorous physical activity >60 minutes per day at T1 showed relationship (β = −1.091, P =.026) with IMT independently of late adolescent physical activity. No significant relationship was present for arterial function. Conclusion: Arterial structure in adolescent males is associated with physical activity at 12 years while relationship with CRF can be seen in late adolescence and cumulatively from puberty to late adolescence. [ABSTRACT FROM AUTHOR]

This prospective cohort study aimed to investigate the association between physical activity (PA) as measured using accelerometers, and functional improvement measured using a short physical performance battery in older patients undergoing rehabilitation. After admission to the rehabilitation hospital, patients were categorized into quartile groups based on their level of PA measured using accelerometers. The primary outcome was physical function measured using the short physical performance battery at hospital discharge. A total of 204 patients were included in the analysis. After adjusting for confounding factors, light-intensity PA (p <.001) and moderate-to-vigorous-intensity PA (p <.001) were associated with a short physical performance battery at hospital discharge. In conclusion, PA at admission is positively associated with functional improvement in older patients undergoing hospital rehabilitation. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate (1) if there are differences in training load and intensity between the different training days within a microcycle and (2) if training load and intensity within the different training days are stable over the course of a season. Data were collected over a full season from a team in the women's premier division in Norway. External load (total distance, high-speed-running distance, sprint distance, and the combined number of accelerations and decelerations [ACCDEC]) was assessed using a 10-Hz GPS system with a built-in accelerometer. Internal load was assessed through session rating of perceived exertion, which was multiplied with session duration (session rating of perceived exertion-load). Training days were classified in relation to their proximity to the upcoming match day (MD): MD − 4, MD − 3, MD − 2, and MD − 1. Contents on these days were standardized according to a weekly periodization model followed by the coaching staff. Differences between training days were analyzed using a linear mixed-effects model. All training days were significantly different from each other across multiple variables. ACCDEC values were highest on MD − 4 (147.5 [13.0] ACCDEC count), and all distance variables were highest on MD − 3. All measures of training load were significantly reduced from MD − 3 to MD − 2 (effect size [ES] = 1.0–4.1) and from MD − 2 to MD − 1 (ES = 1.6-4.3). A significant negative effect across the season was observed for session rating of perceived exertion-load and ACCDEC (ES = 0.8–2.1). These results provide evidence that elite female football teams can be successful in differentiating training load between training days when implementing a weekly periodization approach. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMUs) attached to the distal tibia are a validated method of measuring lower-extremity impact accelerations, called tibial accelerations (TAs), in runners. However, no studies have investigated the effects of small errors in IMU placement, which would be expected in real-world, autonomous use of IMUs. The purpose of this study was to evaluate the effect of a small proximal shift in IMU location on mean TAs and relationships between TAs and ground reaction force loading rates. IMUs were strapped to 18 injury-free runners at a specified standard location (∼1 cm proximal to medial malleolus) and 2 cm proximal to the standard location. TAs and ground reaction forces were measured while participants ran at self-selected and 10% slower/faster speeds. Mean TA was lower at the standard versus proximal IMU location in the faster running condition (P =.026), but similar in the slower (P =.643) and self-selected conditions (P =.654). Mean TAs measured at the standard IMU explained more variation in ground reaction force loading rates (r2 =.79−.90; P <.001) compared with those measured at the proximal IMU (r2 =.65−.72; P <.001). These results suggest that careful attention should be given to IMU placement when measuring TAs during running. [ABSTRACT FROM AUTHOR]

Wrist-worn accelerometry metrics are not well defined in older adults. Accelerometry data from 720 participants (mean age 70 years, 55% women) were summarized into (a) total activity counts per day, (b) active minutes per day, (c) active bouts per day, and (d) activity fragmentation (the reciprocal of the mean active bout length). Linear regression and mixed-effects models were utilized to estimate associations between age and gait speed with wrist accelerometry. Activity counts per day, daily active minutes per day, and active bouts per day were negatively associated with age among all participants, while positive associations with activity fragmentation were only observed among those ≥65 years. More activity counts, more daily active minutes, and lower activity fragmentation were associated with faster gait speed. There were baseline age interactions with annual changes in total activity counts per day, active minutes per day, and activity fragmentation (Baseline age × Time, p <.01 for all). These results help define and characterize changes in wrist-based physical activity patterns among older adults. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the associations between physical activity (PA), sedentary behavior, and executive function in preadolescents. Methods: One hundred and twenty preadolescents were recruited from 2 Hong Kong primary schools. PA and sedentary behavior were recorded for 7 consecutive days by accelerometer. Executive function performance, including inhibition (Stroop task and Flanker task) and working memory (Sternberg paradigm task), were measured. Body mass index and cardiorespiratory fitness (multistage fitness test) were tested. Latent profile analysis explored the profiles of PA and sedentary behavior in preadolescents. Results: Three distinct profiles were identified: low activity, average activity, and high activity. Participants in low activity performed worse in the accuracy of Stroop task (vs average activity, P =.03; vs high activity, P <.01), Flanker task (vs average activity, P =.02; vs high activity, P <.001), and Sternberg paradigm task (vs average activity, P <.01; vs high activity, P <.01). No significant difference was observed between participants with average and high activities. No significant association was observed for profiles on body mass index and cardiorespiratory fitness. Conclusion: Supplementing the consensus of the literature that moderate to vigorous PA benefits cognition, the authors conclude that light PA may also enhance preadolescents' executive functioning. [ABSTRACT FROM AUTHOR]

Purpose: To identify associations between amount of school recess provision and children's physical activity (PA), weight status, adiposity, cardiorespiratory endurance, muscular strength, and muscular endurance. Method: Data from 6- to 11-year-old participants (n = 499) in the 2012 National Youth Fitness Survey were analyzed. Parents/guardians reported children's PA levels and recess provision, categorized as no/minimal (9.0%), low (26.1%), medium (46.0%), or high (18.9%). Children wore a wrist-worn accelerometer for 7 days and completed anthropometric measurements. Fitness was assessed using grip strength and treadmill, pull-up, and plank tests. Cross-sectional linear and logistic regression compared outcomes across levels of recess provision adjusting for the survey's complex sampling design. Results: Children with high provision of recess were 2.31 times more likely to meet PA guidelines according to parent report than those with no/minimal recess. Accelerometer-measured PA followed a more U-shaped pattern, wherein PA was higher in children with high, compared to low, recess provision but comparable to those with no/minimal recess provision. There were no associations with weight status, adiposity, or fitness. Conclusion: Current recess recommendations (20 min·d−1) may be insufficient as 30 minutes per day of recess was associated with a 2-fold greater likelihood of achieving recommended PA levels. Additional research on recess quantity and quality is needed. [ABSTRACT FROM AUTHOR]

Purpose: To quantify match load associated with padel and compare responses with both singles and doubles tennis. Methods: On separate days, 12 participants (7 men and 5 women) played 60-minute padel (PADEL), singles tennis (SINGLES), and doubles tennis (DOUBLES) simulated games. Participants wore a 10-Hz GPS/100-Hz triaxial accelerometer unit and heart-rate monitor. Exercise-related sensations and blood lactate concentration were monitored every 20 minutes. Match-play characteristics (temporal structure) and shot selection were derived from video analysis. Vertical jump ability was assessed before and after each game. Results: Heart rate, exercise-related sensations (overall perceived exertion and limb discomfort), and physical load (total distance covered, PlayerLoad, acceleration density and load) for SINGLES were higher compared with DOUBLES and PADEL (all P ≤.05). Blood lactate concentrations remained low (1–2 mmol·L−1) and did not differ between conditions. Effective playing time (P <.001) was lower in SINGLES and DOUBLES compared with PADEL. The number of forehands (P =.002) and backhands (P <.001) was greater for SINGLES than for DOUBLES and PADEL. The number of volleys/smashes and lobs (P <.05) was greater for PADEL compared with SINGLES and DOUBLES. Performance for squat, countermovement, and multirebound jumps was similarly reduced below baseline after match play (P <.05), independent of condition. Conclusion: Padel imposes a unique match load on players that is different from singles tennis and more closely resembles that of doubles tennis. Cardiovascular stimulation and physical load are highest in singles tennis, while padel sees players hit a larger variety of shots with higher effective playing percentages. [ABSTRACT FROM AUTHOR]

Purpose: To understand how a season of sport education (SE) and a hybrid SE and cooperative learning season impacted on elementary school students' physical activity levels and motivation and to examine possible differences according to gender. Method: A total of 97 fourth- to fifth-grade students in four intact classes participated in a 14-lesson handball season. Results: Students in SE had higher levels of moderate-to-vigorous physical activity levels than the hybrid season. When sorted by gender, boys were significantly more active than girls in both interventions. However, students in the hybrid season reported higher levels of motivation than participants in the SE season, especially for intrinsic motivation. Conclusion: The hybridization of models positively affected students' motivation in PE, while the reverse is true of SE with regard to physical activity levels. [ABSTRACT FROM AUTHOR]

The goal of this study was to examine associations between accelerometer-measured physical activity (PA) and sedentary time (ST) with mortality by a genetic risk score (GRS) for longevity. Among 5,446 women, (mean [SD]: age, 78.2 [6.6] years), 1,022 deaths were observed during 33,350 person-years of follow-up. Using multivariable Cox proportional hazards models, higher light PA and moderate to vigorous PA were associated with lower mortality across all GRS for longevity categories (low/medium/high; all ptrend <.001). Higher ST was associated with higher mortality (ptrend across all GRS categories <.001). Interaction tests for PA and ST with the GRS were not statistically significant. Findings support the importance of higher PA and lower ST for reducing mortality risk in older women, regardless of genetic predisposition for longevity. [ABSTRACT FROM AUTHOR]

Background: Incomplete data due to nonparticipation and noncompliance with the study protocol can bias the results of studies. We investigated how a nationwide accelerometer sample of adolescents and young adults is affected by such incomplete data. Methods: We analyzed cross-sectional data from 6465 participants (11–31 y old) who participated in a national health survey in Germany (KiGGS Wave 2; 2014–2017). The data included information about the participation in the measurement of physical activity using accelerometers, compliance with the wear-time protocol, and sociodemographic and health-related variables. Multivariable regression analyses were conducted to detect factors associated with incomplete data. Results: Of the total sample, 78.0% participated in the accelerometer part of the study, and 83.5% of the participants with data available complied with the wear-time protocol. In 11- to 17-year-olds, the likelihood of having incomplete accelerometer data was higher in boys, older adolescents, adolescents with a lower sociodemographic status, adolescents with overweight, adolescents not participating in organized sport, adolescents not speaking only German at home, current smokers, and adolescents having a higher soft drink consumption. In 18- to 31-year-olds, the likelihood of having incomplete accelerometer data was higher in men, adults with a lower educational level, adults not speaking only German at home, and adults who smoke. Conclusions: Our results suggest that accelerometer samples are biased such that participants with more beneficial health behaviors provide complete accelerometer data more often. This knowledge should be used to design effective recruitment strategies and should be considered when interpreting results of accelerometer studies. [ABSTRACT FROM AUTHOR]

Different starch-to-protein ratios were compared among neutered and spayed domiciled cats. Male and female obese and non-obese cats were fed kibble diets ad libitum for 4 months high in starch (HS (38 % crude protein (CP)): starch 32 %, protein 38 %; DM basis) or high in protein (HP (55 % CP): starch 19 %, protein 55 %) but similar in energy and fat in a crossover design. Physical activity was evaluated using an accelerometer, and body composition (BC), energy expenditure (EE) and water turnover (WT) using the doubly labelled water method. Results were compared in a 2 diet × 2 sex × 2 body condition factorial arrangement. Cats fed the HS (38 % CP) diet maintained a constant body weight, but lean mass (LM) tended to be reduced in female obese but to be increased in male non-obese (P < 0·08) and increased in female non-obese cats (P = 0·01). The HP (55 % CP) diet induced an increase in cat body weight and LM (P < 0·05) without altering BC proportion. EE tended to be higher in males (351 (se 8) kJ/kg0·67/d) than females (330 (se 8) kJ/kg0·67/d; P = 0·06), was unaffected by diet or BC, decreased as age increased (R 2 0·44; P < 0·01) and increased as physical activity increased (R 2 0·58; P < 0 · 01). WT was higher for the HP (55 % CP) diet (P < 0 · 01) and increased with EE (R 2 0·65; P < 0 · 01). The HS (38 % CP) diet favoured body weight control during 4 months of ad libitum feeding. Caution is necessary to balance protein in diets of female obese cats over 5 years, as they may have low energy and food intake, with LM loss. [ABSTRACT FROM AUTHOR]

Background: Assessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales. The purpose of this study was therefore to evaluate the relationship between intermittent measurements of wrist-worn accelerometers and the patient's progression of arm motor function assessed by routine clinical outcome measures during a rehabilitation period. Methods: Patients enrolled in in-hospital rehabilitation following a stroke were invited. Included patients were asked to wear wrist accelerometers for 24 h at the start (T1) and end (T2) of their rehabilitation period. On both occasions arm motor function was assessed by the modified Motor Assessment Scale (M_MAS) and the Motor Activity Log (MAL). The recorded accelerometry was compared to M_MAS and MAL. Results: 20 patients were included, of which 18 completed all measurements and were therefore included in the final analysis. The resulting Spearman's rank correlation coefficient showed a strong positive correlation between measured wrist acceleration in the affected arm and M-MAS and MAL values at T1, 0.94 (p < 0.05) for M_MAS and 0.74 (p < 0.05) for the MAL values, and a slightly weaker positive correlation at T2, 0.57 (p < 0.05) for M_MAS and 0.46 − 0.45 (p = 0.06) for the MAL values. However, no correlation was seen for the difference between the two sessions. Conclusions: The results confirm that the wrist acceleration can differentiate between the affected and non-affected arm, and that there is a positive correlation between accelerometry and clinical measures. Many of the patients did not change their M-MAS or MAL scores during the rehabilitation period, which may explain why no correlation was seen for the difference between measurements during the rehabilitation period. Further studies should include continuous accelerometry throughout the rehabilitation period to reduce the impact of day-to-day variability. [ABSTRACT FROM AUTHOR]

Accelerometers play a crucial role in the railway industry, especially in track monitoring. Traditionally, they are placed on the railway tracks or often on bridges to monitor the health and condition of the infrastructure. Recently, there has been an increased focus on using regular trains to monitor the condition of railway infrastructure. Often, the sensors are placed based on certain assumptions without much scientific evidence or support. This paper utilizes the multibody simulation software GENSYS to identify the optimal placement of accelerometers on a passenger train for monitoring railway switch wear. Switch wear profiles were generated systematically and used as input for the simulations, studying acceleration at a total of 93 locations distributed among the wheelsets, bogies, and carbody. Based on both time and frequency domain analyses, optimal sensor locations were identified, generally close to the first bogie or wheelset at the leading carbody. Accelerations generated by the wheelset passing the switch can also be captured in the carbody, but it is important to note that these are several orders lower in magnitude compared to the acceleration on the wheelset. If accelerometers are to be placed in the carbody, correct sensitivity must be chosen, and a high-pass filter should be applied to capture the acceleration signals associated with switch wear. The study confirms that there is a direct correlation between the depth of switch wear and the magnitude of the acceleration. It remains effective even under various curve radii and train speeds. [ABSTRACT FROM AUTHOR]

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): −37.8–107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: −68.4–73.4 min) and 32.1 min (95% LoA: −54.4–118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19–2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16–1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity. [ABSTRACT FROM AUTHOR]

Thermosphere mass density and crosswind can be derived from accelerometer and GNSS tracking data. However, present datasets are often provided without comprehensive uncertainty specifications. We present a newly developed method that propagates measurement noise and errors in the satellite specification, thermosphere models, and radiation flux data to density observations to quantify their uncertainty. We focus specifically on density observations derived only from GNSS tracking data, which are limited in resolution along the orbit due to unavoidable smoothing. While the method can be applied to simulated and real data, making it useful for existing datasets and mission design, we demonstrated it using data from the GRACE B satellite. First, we compare the aerodynamic acceleration derived separately from the accelerometer and GNSS tracking data, highlighting the role of two significant noise sources: noise due to the differentiation of the positions and noise from the evaluation of the gravity vector at a noisy position. Averaging substantially reduces the noise in the aerodynamic acceleration as long as the differentiation noise dominates, which is the case at frequencies higher than the orbital frequency. Below, gravity vector evaluation noise becomes the dominating noise source, and consequently, averaging over longer periods leads to only marginal uncertainty reduction. Further, we investigate the uncertainty in the radiation pressure acceleration and demonstrate that averaging over one orbit substantially reduces the uncertainty in the along-track radiation pressure acceleration. We show that the uncertainty of density observations derived from the accelerometer data is about 4% of the density for data from 2003 when the GRACE B satellite was at 490 km altitude during high solar activity. In 2008, solar activity was very low, and the altitude was still 476 km, resulting in an uncertainty of 5%–20% because GNSS tracking noise and radiation pressure modeling errors play a much larger role as the aerodynamic acceleration becomes smaller. In the case of density observations derived only from GNSS tracking data, the uncertainty is about 5% in 2003 and 20%–50% in 2008 when averaging over one-third orbit. In 2008, GNSS tracking noise explains nearly all uncertainty in the density observation. Averaging over one orbit reduces the uncertainty to 4% and 5% in 2003 and 2008, respectively. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to identify and validate cut-points for measuring physical activity using Axivity AX6 accelerometers positioned at the shank in older adults. Free-living physical activity was assessed in 35 adults aged 55 and older, where each participant wore a shank-mounted Axivity and a waist-mounted ActiGraph simultaneously for 72 hours. Optimized cut-points for each participant's Axivity data were determined using an optimization algorithm to align with ActiGraph results. To assess the validity between the physical activity assessments from the optimized Axivity cut-points, a leave-one-out cross-validation was conducted. Bland-Altman plots with 95% limits of agreement, intraclass correlation coefficients (ICC), and mean differences were used for comparing the systems. The results indicated good agreement between the two accelerometers when classifying sedentary behaviour (ICC = 0.85) and light physical activity (ICC = 0.80), and moderate agreement when classifying moderate physical activity (ICC = 0.67) and vigorous physical activity (ICC = 0.70). Upon removal of a significant outlier, the agreement was slightly improved for sedentary behaviour (ICC = 0.86) and light physical activity (ICC = 0.82), but substantially improved for moderate physical activity (ICC = 0.81) and vigorous physical activity (ICC = 0.96). Overall, the study successfully demonstrated the capability of the resultant cut-point model to accurately classify physical activity using Axivity AX6 sensors placed at the shank. [ABSTRACT FROM AUTHOR]

Piezoelectric accelerometers excel in vibration sensing. In the emerging trend of fully organic electronic microsystems, polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals, such as vocal vibrations in wearable speaking assistants for those with speaking difficulties. However, high-performance polymeric piezoelectric accelerometers suitable for such applications are rare. Piezoelectric organic compounds such as PVDF have inferior properties to their inorganic counterparts such as PZT. Consequently, most existing polymeric piezoelectric accelerometers have very unbalanced performance metrics. They often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers, leading to increased noise density and limited application potentials. In this study, a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is introduced. This new design aims to simultaneously achieve high sensitivity, broad bandwidth, and low noise. Five samples were manufactured and characterized, demonstrating an average sensitivity of 29.45 pC/g within a ± 10 g input range, a 5% flat band of 160 Hz, and an in-band noise density of 1.4 µg/ Hz . These results surpass those of many PZT-based piezoelectric accelerometers, showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems. [ABSTRACT FROM AUTHOR]

Background: Accelerometers are commonly used for the assessment of PA; however, these devices have not been validated in people with dystonia who experience movement limitations. To properly understand movement behaviors and deliver accurate exercise prescription in this population, the validity of these devices must be tested. Objective: This study aimed to validate step count and postural transitions detected by the activPAL accelerometer (AP) against direct observation (DO) during two functional assessments: the 30-s sit-to-stand (30STS) and 6-min usual-pace walk tests. Methods: A total of 11 participants with cervical dystonia (CD) (male/female n = 5/6; mean age = 61 years; BMI = 24 kg/m²) performed the 6-min usual pace walking and 30STS while wearing the activPAL. A trained observer counted steps and observed the number of sit-to-stands. Results: The average step count detected with AP and DO was 651.8 (218-758) and 654.5 (287-798) respectively. The average transitions detected were 11 (4-16) and 12 (4-17) respectively. Both methods showed good agreement and there was a statistically significant and strong correlation between the two methods, i.e., transitions (r = 0.983, p = 0.0001), and step counts (r = 0.9841, p = 0.0001). Conclusion: There is a good agreement between activPAL and direct observation for step counts and transitions between sitting and standing in people living with CD. [ABSTRACT FROM AUTHOR]

Introduction: Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices. Methods: The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults. Results: The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females). Conclusions: The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases. [ABSTRACT FROM AUTHOR]

Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices. [ABSTRACT FROM AUTHOR]

Measurement of vibration without having contact with the target is crucial for many applications. However, real time vibration signals are frequently mixed with various levels of dynamic noises. In this research, a non-contact type vibration measurement system based on laser triangulation method is proposed by using a PSD (Position sensitive detector) and here a single axis Brüel and Kjaer piezoelectric accelerometer is used to validate the experimental results. The primary goal of this measurement system is to develop a Gabor Transform based signal denoising technique, in order to eliminate noise from the real-time vibration data. Furthermore, PSD platform vibration, which might cause unanticipated inaccuracies in the detected vibration data, has also been eliminated by utilizing a self-vibration compensation approach using an ADXL-345 three-axis accelerometer. The self-vibration compensated vibration measurement technique has been designed using an NI (national instruments) cRIO (CompactRIO) 9074 real-time FPGA (field programmable gate array) based embedded controller. Consequently, a Gabor Transform and Expansion-based Signal Processing Technique is designed using NI cRIO-9074 real time platform to reduce external noise from the self-vibration compensated vibration data. It has been observed that the implementation of Gabor Transform has improved the performance of the system in term of enhancing the SNR (Signal-to-noise ratio). [ABSTRACT FROM AUTHOR]

Background: There is a growing population of survivors of colorectal cancer (CRC). Fatigue and insomnia are common symptoms after CRC, negatively influencing health-related quality of life (HRQoL). Besides increasing physical activity and decreasing sedentary behavior, the timing and patterns of physical activity and rest over the 24-h day (i.e. diurnal rest-activity rhythms) could also play a role in alleviating these symptoms and improving HRQoL. We investigated longitudinal associations of the diurnal rest-activity rhythm (RAR) with fatigue, insomnia, and HRQoL in survivors of CRC. Methods: In a prospective cohort study among survivors of stage I-III CRC, 5 repeated measurements were performed from 6 weeks up to 5 years post-treatment. Parameters of RAR, including mesor, amplitude, acrophase, circadian quotient, dichotomy index, and 24-h autocorrelation coefficient, were assessed by a custom MATLAB program using data from tri-axial accelerometers worn on the upper thigh for 7 consecutive days. Fatigue, insomnia, and HRQoL were measured by validated questionnaires. Confounder-adjusted linear mixed models were applied to analyze longitudinal associations of RAR with fatigue, insomnia, and HRQoL from 6 weeks until 5 years post-treatment. Additionally, intra-individual and inter-individual associations over time were separated. Results: Data were available from 289 survivors of CRC. All RAR parameters except for 24-h autocorrelation increased from 6 weeks to 6 months post-treatment, after which they remained relatively stable. A higher mesor, amplitude, circadian quotient, dichotomy index, and 24-h autocorrelation were statistically significantly associated with less fatigue and better HRQoL over time. A higher amplitude and circadian quotient were associated with lower insomnia. Most of these associations appeared driven by both within-person changes over time and between-person differences in RAR parameters. No significant associations were observed for acrophase. Conclusions: In the first five years after CRC treatment, adhering to a generally more active (mesor) and consistent (24-h autocorrelation) RAR, with a pronounced peak activity (amplitude) and a marked difference between daytime and nighttime activity (dichotomy index) was found to be associated with lower fatigue, lower insomnia, and a better HRQoL. Future intervention studies are needed to investigate if restoring RAR among survivors of CRC could help to alleviate symptoms of fatigue and insomnia while enhancing their HRQoL. Trial registration: EnCoRe study NL6904 (https://www.onderzoekmetmensen.nl/). [ABSTRACT FROM AUTHOR]

To investigate a smartphone-enabled quantitative evaluation of anticipatory postural adjustments (APA) during one-leg stance (OLS) movements among individuals with Parkinson's disease (PD). This cross-sectional study included 10 young controls, 10 older individuals, and 13 individuals with PD. A smartphone and accelerometer were attached to the participants' lower back (L5), and the movements of the lower back toward the stance side during OLS were measured. For acceleration, the time to the peak value in the stance direction (peak latency [PL]) and the amount of displacement to the peak value in the stance direction (peak magnitude [PM]) were analyzed as APA characteristics. Additionally, the measured PL was divided by the PM for each group to obtain the APA ratio (APAr) as a new index. Individuals with PD showed a delayed PL and decreased PM (vs. young controls: p =.002 for PL, p <.001 for PM) (vs. older individuals: p =.022 for PL, p =.001 for PM). The APAr clustered the young controls, older individuals, and individuals with PD. According to the receiver operating characteristic curve the APAr value was 0.95, and individuals in the PD group were identified (i.e. area under the curve: 0.98; sensitivity: 85.0%; specificity: 100%). Moreover the APAr was correlated with severity and balance ability in individuals with PD (p =.015 for NFOG-Q, p =.028 for UPDRS, p =.036 for TUG, p =.015 for Mini-BESTest, p =.018 for OLS time). This smartphone-based evaluation using the APAr index was reflective of disease severity and decreased balance ability among individuals with PD. The facilitation of this measurement can help clinicians and physiotherapists quantitatively evaluate the APA of individuals with PD at laboratories and hospitals as well as in home environments. [ABSTRACT FROM AUTHOR]

This paper reports a MEMS accelerometer based on bulk SiC. The fabrication and packaging scheme were designed, an efficient signal processing circuit was built, and the output performance of the sensor was tested. The test results show that the resistivity of the piezoresistive material is 194.40 Ω μm, the sensor's output sensitivity is 0.21 mV/g, the linearity is 99.8%, and the resonance frequency is 1636 Hz. Finally, the accelerometer was applied to the two ground vibration tests of aero‐engine, and the vibration signal of the turbine components and the test bed was successfully extracted. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Combining elastic full waveform inversion (FWI) with rock physics holds promise for expanding the application of FWI beyond seismic imaging to predicting and monitoring reservoir properties. Distributed acoustic sensing (DAS), a rapidly developing seismic acquisition technology, is being explored for its potential in supporting FWI applications. In this study, we implement a sequential inversion scheme that integrates elastic FWI and Bayesian rock physics inversion, using a vertical seismic profile (VSP) data set acquired with accelerometer and collocated DAS fibre at the Carbon Management Canada's Newell County Facility. Our aim is to establish a baseline model of porosity and lithology parameters to support later monitoring of CO2 storage. Key strategies include an effective source approach for addressing near-surface complications, a modelling strategy to simulate DAS data comparable to field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We conduct FWI tests on accelerometer, DAS, and combined accelerometer-DAS data. While our inversion results accurately reproduce either data set, the elastic models inverted from accelerometer data outperform the other two in matching well logs and identifying the target reservoir. We attribute this outcome to the limited complementarity of DAS data with accelerometer data in our experiment, along with the limitations imposed by single-component measurements on DAS. The porosity and lithology models predicted from accelerometer-derived elastic models are reasonably accurate at the well location and exhibit geologically meaningful spatial distribution. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular (CV) morbidity after kidney transplantation (KTx) in childhood is of increasing importance. In light of a high prevalence of CV risk factors, protective measures such as physical activity (PA) come into focus. Our aim was to comprehensively assess PA in pediatric KTx recipients and evaluate its impact on CV health. Methods: Forty-eight patients were assessed for frequency, duration, intensity, and setting of PA using the "Motorik–Modul" PA questionnaire. Walking-based activity was measured by accelerometer in a subgroup (n = 23). CV risk factors and subclinical CV organ damage were determined. The impact of PA on CV parameters was analyzed using linear regression models. Results: Fifty-two percent of pediatric KTx recipients did not reach WHO recommended PA level; 54% did not engage in PA with vigorous intensity (VPA). Twenty-nine percent indicated an extremely inactive lifestyle (< 120 min/week of moderate to vigorous intensity PA, MVPA). Compared to the healthy German KiGGS cohort, KTx recipients specifically lacked engagement in sport activities (KTx: 129 min/week; 95%CI, 97–162 vs. KiGGS, 242 min/week; 95%CI, 230–253). VPA was associated with lower systolic blood pressure (p = 0.024) and resting heart rate (p = 0.005), MVPA with fewer components of the post-transplant metabolic syndrome (p = 0.037), and better left ventricular diastolic function (p = 0.006). Conclusions: A considerable lack of PA, especially VPA, exists in young KTx recipients. PA was positively associated with important parameters of CV health. While long-term CV protection through PA seems promising in pediatric KTx recipients, specific educational approaches are most likely needed to increase patients' engagement in sport activities. [ABSTRACT FROM AUTHOR]

This study aimed to reveal the key lifestyle elements that improve physical and mental health in university students by focusing on physical activity, nutrition, and sleep. This cross-sectional study was conducted between October 2021 and December 2021. The participants were 290 first-year students (mean age, 18.63 ±.63 years; age range, 18 to 23; 198 female). The outcomes were daily step counts measured using accelerometers, dietary intake by nutrient category, sleep duration, subjective sleep quality, exercise frequency and duration by exercise type, screen time, depression level, and subjective fatigue by body part. Depression and subjective eye fatigue represent mental and physical health outcomes. Subjective sleep quality predicted depression (β = −1.22, P <.001) and eye fatigue (β = −.23, P <.01) in the path analysis. Participants with higher subjective sleep quality performed more frequent aerobic exercise (P <.01), longer session times of physical relaxation exercise (P <.05), and shorter screen time (P <.05). Subjective sleep quality could be a key factor for high mental and physical health. Furthermore, performing aerobic and relaxation exercises and reducing screen time are important for improving the subjective sleep quality. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the long-term effect of a tablet-based, cognitive-behavioral group intervention (Tab-G) to improve daily walking for older adults with arthritis. Method: Using an experimental pretest/posttest repeated measure design, long-term effects on step count, fatigue, self-efficacy, and quality of life (QOL) were investigated. Results: Results of repeated measures analysis of variance showed significant improvement in step counts (F[1, 37] = 4.18, p = 0.048), fatigue (F[1, 36] = 9.971, p = 0.003), self-efficacy (F[1,28] = 4.645, p = 0.04), and QOL (F[1, 29] = 6.147, p = 0.019) in the Tab-G group compared to the control group. There were significant time effects across four time points (baseline and Weeks 4, 8, and 10) in fatigue (F[3, 108] = 5.43, p = 0.002), self-efficacy (F[3, 84] = 5.433, p = 0.002), and QOL (F[3, 87] = 3.673, p = 0.015), but not in step counts (F[3, 111] = 0.611, p = 0.609). Conclusion: Findings demonstrate positive long-term effects on fatigue in older adults with arthritis. [Journal of Gerontological Nursing, 50(5), 35–42.] [ABSTRACT FROM AUTHOR]

The current study aimed to describe sleep habits and analyze the associations between sleep indices, changes in perceived fatigue (Δ Fatigue) and external training load measures in women soccer players during a 7-day training camp. Sixteen elite women soccer players (age: 25.4 ± 3.6 years; mean ± SD) from the Portuguese Women's National Team participated in the study. Sleep indices (sleep duration and efficiency) were measured using a wrist-worn accelerometer. External training loads measures were measured using GPS devices. Players also reported perceived fatigue using a Likert scale (1 - very, very low to 7 – very, very high) before and immediately after sleep. Players' within-subject coefficient of variation for sleep duration was 5.6%, and 4.6% for sleep efficiency. Individually, 8 players (50%) slept less than 7 h per night throughout the training camp, and the same number of athletes had a sleep efficiency lower than 85%. Similar values (p<0.05) were measured for sleep duration and efficiency between training and match days. A moderate negative within-subjects correlation was found between Δ Fatigue and sleep duration (adjusted for pre-sleep fatigue) [r = −0.32; 95% Confidence Interval (−0.51 – −0.08); p = 0.04]. These findings reinforce the importance of sleep in the recovery process of elite women soccer players, showing that more sleep may help to attenuate fatigue. [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 [ABSTRACT FROM AUTHOR]

Introduction: Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test--retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke. Methods: Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems. Results and discussion: Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of -0.01 s). Bland-- Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test--retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke. [ABSTRACT FROM AUTHOR]

Shoe-based wearable sensor systems are a growing research area in health monitoring, disease diagnosis, rehabilitation, and sports training. These systems—equipped with one or more sensors, either of the same or different types—capture information related to foot movement or pressure maps beneath the foot. This captured information offers an overview of the subject's overall movement, known as the human gait. Beyond sensing, these systems also provide a platform for hosting ambient energy harvesters. They hold the potential to harvest energy from foot movements and operate related low-power devices sustainably. This article proposes two types of strategies (Strategy 1 and Strategy 2) for an energy-autonomous shoe-based system. Strategy 1 uses an accelerometer as a sensor for gait acquisition, which reflects the classical choice. Strategy 2 uses a piezoelectric element for the same, which opens up a new perspective in its implementation. In both strategies, the piezoelectric elements are used to harvest energy from foot activities and operate the system. The article presents a fair comparison between both strategies in terms of power consumption, accuracy, and the extent to which piezoelectric energy harvesters can contribute to overall power management. Moreover, Strategy 2, which uses piezoelectric elements for simultaneous sensing and energy harvesting, is a power-optimized method for an energy-autonomous shoe system. [ABSTRACT FROM AUTHOR]

Background: Actigraphy is often used to measure sleep in pediatric populations, despite little confirmatory evidence of the accuracy of existing sleep/wake algorithms. The aim of this study was to determine the performance of 11 sleep algorithms in relation to overnight polysomnography in children and adolescents. Methods: One hundred thirty-seven participants aged 8–16 years wore two Actigraph wGT3X-BT (wrist, waist) and three Axivity AX3 (wrist, back, thigh) accelerometers over 24-h. Gold standard measures of sleep were obtained using polysomnography (PSG; Embletta MPRPG, ST + Proxy and TX Proxy) in the home environment, overnight. Epoch by epoch comparisons of the Sadeh (two algorithms), Cole-Kripke (three algorithms), Tudor-Locke (four algorithms), Count-Scaled (CS), and HDCZA algorithms were undertaken. Mean differences from PSG values were calculated for various sleep outcomes. Results: Overall, sensitivities were high (mean ± SD: 91.8%, ± 5.6%) and specificities moderate (63.8% ± 13.8%), with the HDCZA algorithm performing the best overall in terms of specificity (87.5% ± 1.3%) and accuracy (86.4% ± 0.9%). Sleep outcome measures were more accurately measured by devices worn at the wrist than the hip, thigh or lower back, with the exception of sleep efficiency where the reverse was true. The CS algorithm provided consistently accurate measures of sleep onset: the mean (95%CI) difference at the wrist with Axivity was 2 min (-6; -14,) and the offset was 10 min (5, -19). Several algorithms provided accurate measures of sleep quantity at the wrist, showing differences with PSG of just 1–18 min a night for sleep period time and 5–22 min for total sleep time. Accuracy was generally higher for sleep efficiency than for frequency of night wakings or wake after sleep onset. The CS algorithm was more accurate at assessing sleep period time, with narrower 95% limits of agreement compared to the HDCZA (CS:-165 to 172 min; HDCZA: -212 to 250 min). Conclusion: Although the performance of existing count-based sleep algorithms varies markedly, wrist-worn devices provide more accurate measures of most sleep measures compared to other sites. Overall, the HDZCA algorithm showed the greatest accuracy, although the most appropriate algorithm depends on the sleep measure of focus. [ABSTRACT FROM AUTHOR]

To describe physical functioning after severe COVID-19-infection. An explanatory sequential mixed method design was used. Thirty-nine participants performed tests and answered questionnaires measuring physical functioning six months after hospitalisation due to COVID-19. Thirty of these participants participated in semi-structured interviews with questions regarding how they perceived their physical functioning and recovery from COVID-19 at 12 months post-hospitalisation. At six months, physical functioning measured via chair stand test and hip-worn accelerometers was lower than normal reference values. There was a reduction in breathing muscle strength. Participants estimated their functional status during different activities as lower compared to those before COVID-19-infection, measured with a patient-specific functional scale. At one year after infection, there were descriptions of a rough recovery process and remaining symptoms. Patients recovering from severe COVID-19 seem to have reduced physical functioning and activity levels, and they perceive their recovery to be slow and difficult. They experienced a lack of clinical support and contradictory advice regarding rehabilitation. Coaching in returning to physical functioning after the infection needs to be better co-ordinated and there is a need for guidelines for health professionals to avoid patients receiving contradictory advice. Coronavirus infection disease-19 (COVID-19) can have a great impact on a person's physical functioning. In the early course of the COVID-19 pandemic there were not any clear guidelines regarding rehabilitation for this group of patients. As some people with COVID-19 may have impairments in their physical functioning up to one year after leaving hospital there is a need to provide rehabilitation. [ABSTRACT FROM AUTHOR]

Background: Esports is competitive video gaming, performed within teams or individually, across multiple genres. Players are required to be sedentary for extended periods and require a high-level of cognitive skills for successful competitive performance. There are conflicting findings within the physical activity research in the esports industry. The aim of this research is to explore self-reported physical activity through accelerometer-assessed physical activity, to gain a better insight into the physical activity behaviours of international e'athletes. Method: Participants (n = 796) across multiple popular esports games, holding any in-game rank, competing at any level, were recruited. The survey consisted of demographic details, esports experience, the International Physical Activity Questionnaire-Long Form (IPAQ-LF), and Behavioural Regulations towards Exercise Questionnaire (BREQ-3). Within a convenience sample, local intervarsity e'athletes (n = 18) were recruited to wear a wrist-worn accelerometer to measure physical activity for 7-days and then complete the survey. Results from the accelerometers were compared to the survey results to explore physical activity reporting within this population. Results: When comparing IPAQ-LF to accelerometer data, players significantly over-report moderate-to-vigorous physical activity and weekly MET-min− 1 (p =.018, r =.63 and p ≤.001, r =.92). The BREQ-3 showed that e'athletes categorised as high physical activity displayed significantly higher levels of intrinsic motivation, when compared to players categorised as low and moderate physical activity. Conclusions: E'athletes significantly over report physical activity time when measured through the IPAQ-LF, suggesting previous surveys may overestimate physical activity and further research is needed. Given the exponential growth of the industry and the level of physical inactivity, esports may contribute to global physical inactivity levels. Key Points: Despite in-game rank not influencing physical activity time or motivation, higher levels of intrinsic motivation were associated with increased physical activity among e'athletes. This suggests the importance of fostering intrinsic motivation to promote sustained engagement in physical activity within esports communities. The study reveals concerning levels of sedentary behaviour among e'athletes, with implications for their overall health and well-being. Addressing sedentary behaviour and promoting light physical activity are crucial for mitigating associated health risks within the esports population. The study highlights significant disparities between self-reported physical activity data and accelerometer-assessed data among e'athletes, indicating potential over-estimation of physical activity levels when self-reported. [ABSTRACT FROM AUTHOR]

Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset—a 700,000 person-days unlabelled dataset—in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5–130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve. [ABSTRACT FROM AUTHOR]

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process. [ABSTRACT FROM AUTHOR]

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and noncough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The bestperforming model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector. [ABSTRACT FROM AUTHOR]

This study examined the concordance between accelerometry-measured and self-reported physical activity (PA) and sedentary time in adults with autism. Twenty-four participants wore an ActiGraph GT3X + accelerometer for seven consecutive days and completed the International Physical Activity Questionnaire-Short Form (IPAQ-SF) on the last day of their study participation. Bland-Altman plots assessed the magnitude of agreement between the two measures. Nearly 80% of the participants accumulated the recommended ≥ 150 min of moderate to vigorous PA (MVPA)/week, but were also sedentary for over nine hours/day according to accelerometry data. Findings showed that adults with autism tended to overreport MVPA (b = 1.606, p < 0.01) and underreport sedentary time (b = 1.161, p = 0.03) via the IPAQ-SF, as compared to objective measurements. [ABSTRACT FROM AUTHOR]

The reduction of non‐gravitational accelerations through an onboard accelerometer is a vital part of accurately estimating gravity fields for Gravity Recovery and Climate Experiment (GRACE) Follow‐On (GRACE‐FO). Current accelerometer quality, specifically a defect on the GRACE‐D accelerometer, necessitates the use of extensive calibration techniques to optimally utilize the accelerometer measurements. This paper describes these techniques and correction algorithms as they are used for the official GRACE‐FO accelerometer products, which rest on the information present in the partially functional GRACE‐D accelerometer. First, transplanted data from GRACE‐C is used to remove common effects between the spacecraft. Then, GRACE‐D data is utilized to compute a correction to the transplanted data to account for solar radiation pressure (SRP) and albedo using a parity argument and to account for non‐common drag and thruster leaks in the attitude control system. The SRP and albedo term predominantly improves C3,0 while the drag and leaky thruster term improves the gravity field across the entire spectrum. Finally, some comments on alternative data usage, initial modifications to the algorithms to account for other spacecraft operational modes, and future outlook are provided. Key Points: GRACE‐D accelerometer data, while noisy, is calibrated to recover useable signals and optimize science dataUpdated accelerometer calibrations improve gravity field recovery [ABSTRACT FROM AUTHOR]