Association of Accelerometer-Determined Physical Activity and Sedentary Behavior With the Gut Microbiome in Middle-Aged Women: A Compositional Data Approach.

The beneficial effects of physical activity (PA) on gut microbiome have been reported, nevertheless the findings are inconsistent, with the main limitation of subjective methods for assessing PA. It is well accepted that using an objective assessment of PA reduces the measurement error and also allows objective assessment of sedentary behavior (SB). We aimed to study the associations between accelerometer-assessed behaviors (i.e., SB, light-intensity physical activity [LPA] and moderate-to-vigorous physical activity [MVPA]) with the gut microbiome using compositional data analysis, a novel approach that enables to study these behaviors accounting for their inter-dependency. This cross-sectional study included 289 women from the Northern Finland Birth Cohort 1966. Physical activity was measured during 14 days by wrist-worn accelerometers. Analyses based on the combined effect of MVPA and SB, and compositional data analyses in association with the gut microbiome data were performed. The microbial alpha- and beta-diversity were not significantly different between the MVPA-SB groups, and no differentially abundant microorganisms were detected. Compositional data analysis did not show any significant associations between any movement behavior (relative to the others) on microbial alpha-diversity. Butyrate-producing bacteria such as Agathobacter and Lachnospiraceae CAG56 were significantly more abundant when reallocating time from LPA or SB to MVPA (γ = 0.609 and 0.113, both p-values = 0.007). While PA and SB were not associated with microbial diversity, we found associations of these behaviors with specific gut bacteria, suggesting that PA of at least moderate intensity (i.e., MVPA) could increase the abundance of short-chain fatty acid-producing microbes.

Accelerometer-Measured Daily Behaviors That Mediate the Association Between Refractive Status and Depressive Disorders.

Purpose: To identify the accelerometer-measured daily behaviors that mediate the association of refractive status with depressive disorders and enhance the understanding of behavioral differences in depression. Methods: Participants with baseline mean spherical equivalent (MSE) and 7-day accelerometer measurements from the UK Biobank were included in this cohort study. Refractive status was categorized as hyperopia and non-hyperopia. Four daily behaviors, including moderate to vigorous intensity physical activity (MVPA), light physical activity (LPA), sedentary, and sleep were recorded between 2013 and 2015. We also assessed 24-hour behavior patterns. Depression cases were defined through both questionnaires and hospital records over 10 years of follow-up. Results: Among 20,607 individuals, every 0.5-diopter increase in MSE was associated with a 6% higher risk of depressive disorders, with hyperopia participants at a higher risk than non-hyperopia participants (odds ratio, 1.14; 95% confidence interval, 1.05-1.23; P = 0.001). MVPA and sleep time significantly correlated with depressive disorders, with odds ratios of 0.79 and 1.14 (P < 0.05). MSE showed significant correlations with all four behaviors. The effects of MVPA and sleep duration on MSE and depressive disorders varied throughout the day. Mediation analyses showed that MVPA and sleep partially mediated the relationship between MSE and depressive disorders, with 35.2% of the association between moderate to high hyperopia and depression mediated by MVPA. Conclusions: Physical activity and sleep significantly mediate the relationship between MSE and depressive disorders. Translational Relevance: The mediation effect of MVPA highlights its therapeutic potential in reducing the risk of depression among individuals with moderate to severe hyperopia. Interventions aimed at increasing daytime MVPA and decreasing daytime sleep could enhance mental health in this vulnerable group.

Associations between accelerometer-measured physical activity and sedentary behaviour with physical function among older women: a cross-sectional study.

BACKGROUND: This study aimed to investigate the relationships between accelerometer-measured physical activity (PA) and sedentary behaviour (SB) with physical function (PF) among older Chinese women in the community. METHODS: The present study comprised 1,113 community-dwelling older females, with an average age of 65 ± 2 years. We employed a linear regression analysis to investigate the relationship between patterns of PA and SB with PF. PA variables consisted of total PA time, bouted PA time (a continuous PA that lasts equal to or more than 10 min), and sporadic PA time (a continuous PA that lasts less than 10 min). SB variables included total SB time, 30-min bout of SB (a continuous SB that lasts equal to or more than 30 min), and 60-min bout of SB (a continuous SB that lasts equal to or more than 60 min). PF variables comprised handgrip strength (HGS), one-legged stance test with eyes closed (OLSTEC), usual walking speed (UWS), maximum walking speed (MWS) and chair-stand time (CT). To explore the joint effects of moderate-to-vigorous-intensity PA (MVPA) and SB on PF, we divided the duration of SB and MVPA participation in older women into different combinations: low MVPA & high SB, low MVPA & low SB, high MVPA & high SB, high MVPA & low SB. RESULTS: The study revealed a significant association between 30-min bout of SB and CT, which remained after adjusting for total MVPA time (P = 0.021). Both total MVPA and bouted MVPA were found to be positively associated with better UWS, MWS, CT, and PF Z-score. When the combination of low MVPA & high SB was used as a reference, the regression coefficients for PF ascended by 1.32 (P < 0.001) in the high MVPA & high SB group and by 1.13 (P < 0.001) in the high MVPA & low SB group. CONCLUSIONS: A significant association was observed between poorer lower limb function and prolonged, uninterrupted SB in older women, rather than with the total SB time. Concurrently, the insufficient engagement in MVPA may also be a crucial factor contributing to poorer PF in older women. Engaging in longer durations and higher intensity of PA, such as bouts of MVPA lasting a minimum of 10 min or longer, may contribute to better PF.

Device-measured sedentary time and intensity-specific physical activity in relation to all-cause and cardiovascular disease mortality: the UK Biobank cohort study.

BACKGROUND AND AIMS: Understanding the amounts of intensity-specific movement needed to attenuate the association between sedentary time and mortality may help to inform personalized prescription and behavioral counselling. Herein, we examined the joint associations of sedentary time and intensity-specific physical activity with all-cause and cardiovascular disease (CVD) mortality. METHODS: Prospective cohort study including 73,729 adults from the UK Biobank who wore an Axivity AX3 accelerometer on their dominant wrist for at least 3 days, being one a weekend day, between June 2013 and December 2015. We considered the median tertile values of sedentary time and physical activity in each intensity band to determine the amount of physical activity needed to attenuate the association between sedentary time and mortality. RESULTS: During a median of 6.9 years of follow-up (628,807 person-years), we documented 1521 deaths, including 388 from CVD. Physical activity of any intensity attenuated the detrimental association of sedentary time with mortality. Overall, at least a median of 6 min/day of vigorous physical activity, 30 min/day of MVPA, 64 min/day of moderate physical activity, or 163 min/day of light physical activity (mutually-adjusted for other intensities) attenuated the association between sedentary time and mortality. High sedentary time was associated with higher risk of CVD mortality only among participants with low MVPA (HR 1.96; 95% CI 1.23 to 3.14). CONCLUSIONS: Different amounts of each physical activity intensity may attenuate the association between high sedentary time and mortality.

Associations of accelerometer-measured physical activity and sedentary time with renal function and chronic kidney disease: a national population-based study.

Introduction: There is limited information about the relationship between physical activity (PA) and sedentary behaviors in chronic kidney disease (CKD). Therefore, this study aims to explore the associations of accelerometer-measured PA and sedentary behaviors with CKD. Methods: A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey in the 2003-2004 and 2005-2006 survey cycles. A uniaxial accelerometer measured physical activity (PA) and sedentary time (ST). The associations of PA and ST with estimated glomerular filtration rate (eGFR) and odds of CKD adopted the generalized linear regression, multivariable logistic regression, and isotemporal substitution models. Results: A total of 5,990 adults with 605 CKD patients were included in this study. Compared with the individuals in the first quartile group, participants in the fourth quartile of low-intensity physical activity (LIPA), moderate to vigorous physical activity (MVPA), and ST were associated with 52% (35%, 65%) and 42% (14%, 62%) lower odds of CKD and 64% (17%, 131%) higher odds of CKD, respectively. Substituting 30 min/day of ST with equivalent LIPA/MVPA contributed to risk reduction in CKD. Discussion: The findings suggest that increased LIPA and MVPA and reduced ST were associated with a lower risk of CKD and that replacing ST with LIPA may decrease the risk of CKD.

Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study.

Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio® XT Patch [aged 72-94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (ß = 0.30, 95% CI: 0.16-0.44) and executive function scores (ß = 0.38, 95% CI: 0.22-0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22-0.67) or dementia (OR = 0.25, 95% CI: 0.08-0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia.

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

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

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

Post-discharge sedentary behavior and light-intensity physical activity-associated stroke recurrence in patients with minor ischemic stroke: A preliminary retrospective observational study.

BACKGROUND AND PURPOSE: Evidence regarding whether reducing sedentary behavior and increasing physical activity levels to prevent stroke recurrence is insufficient. Therefore, this study preliminarily investigated whether post-discharge sedentary behavior and physical activity levels in patients with minor ischemic stroke were associated with stroke recurrence. METHODS: This retrospective observational study included 73 patients (aged 72.0 years) with minor ischemic stroke from a previous study. The outcome was recurrent stroke 2 years after stroke onset, assessed using medical records. Exposure factors including sedentary behavior and physical activity levels 6 months post-discharge were measured using accelerometers; patients were classified into the recurrence or non-recurrence groups. Logistic regression analyses were then conducted to determine whether sedentary behavior and physical activity 6 months after discharge were associated with stroke recurrence. RESULTS: Six patients experienced stroke recurrence (recurrence rate, 8.2%). The recurrence group showed greater sedentary behavior (recurrence group 68.0%, non-recurrence group 52.0%, p = 0.007) and less light-intensity physical activity (LPA) (recurrence group 21.0%, non-recurrence group 37.0%, p = 0.002) than in the non-recurrence group. Logistic regression analysis showed that sedentary behavior (odds ratio = 1.083, 95% confidence interval = 1.007-1.165, p = 0.032) and LPA (odds ratio = 0.874, 95% confidence interval = 0.785-0.975, p = 0.015) were independent factors for recurrence of stroke. DISCUSSION: Post-discharge sedentary behavior and LPA in patients with minor ischemic stroke were associated with stroke recurrence. Results suggest that reducing post-discharge sedentary behavior and increasing LPA may be crucial for reducing the risk of stroke recurrence in patients with minor ischemic stroke.

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

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

Temporal relationships in the movement behaviour of adolescents: Testing and methodological considerations of the ActivityStat hypothesis.

The study aimed to investigate the ActivityStat hypothesis by examining the presence and the timeframe of the temporal relationships among physical activity (PA) levels and stationary time (ST) in adolescents. A secondary analysis was performed on data involving 356 adolescents in Dunedin, New Zealand. Participants wore a waist-worn accelerometer for several consecutive days to assess PA levels [i.e. light PA (LPA), and moderate-to-vigorous PA (MVPA)] and ST. Bayesian continuous-time structural equation modelling (CT-SEM) was used to examine the relationship between the behaviours over time and the timeframe during which these relationships occur. Increases in LPA, MVPA, and ST were positively associated with their later behaviours until 2.5, 1.7, and 2 days later, respectively. A cross-behavioural reciprocal and negative relationship between LPA and ST was demonstrated 0.4 days later. A positive relationship between ST and MVPA was observed until about 0.4 days later. This is the first study to investigate the ActivityStat hypothesis using Bayesian CT-SEM in adolescents, examining the multivariate relationships among different behaviours and the associated timeframes. To conclude, evidence of activity synergy was suggested for the within-behavioural relationships, while behavioural compensation was noted for ST. Thus, the findings provide some support for the ActivityStat hypothesis in adolescents.

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types.

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.

Improving the identification of bone-specific physical activity using wrist-worn accelerometry: A cross-sectional study in 11-12-year-old Australian children.

Physical activity (PA) during childhood and adolescence is important for the accrual of maximal peak bone mass. The precise dose that benefits bone remains unclear as methods commonly used to analyze PA data are unsuitable for measuring bone-relevant PA. Using improved accelerometry methods, this study identified the amount and intensity of PA most strongly associated with bone outcomes in 11-12-year-olds. Participants (n = 770; 382 boys) underwent tibial peripheral quantitative computed tomography to assess trabecular and cortical density, endosteal and periosteal circumference and polar stress-strain index. Seven-day wrist-worn raw acceleration data averaged over 1-s epochs was used to estimate time accumulated above incremental PA intensities (50 milli-gravitational unit (mg) increments from 200 to 3000 mg). Associations between time spent above each 50 mg increment and bone outcomes were assessed using multiple linear regression, adjusted for age, sex, height, weight, maturity, socioeconomic position, muscle cross-sectional area and PA below the intensity of interest. There was a gradual increase in mean R2 change across all bone-related outcomes as the intensity increased in 50 mg increments from >200 to >700 mg. All outcomes became significant at >700 mg (R2 change = 0.6%-1.3% and p = 0.001-0.02). Any further increases in intensity led to a reduction in mean R2 change and associations became non-significant for all outcomes >1500 mg. Using more appropriate accelerometry methods (1-s epochs; no a priori application of traditional cut-points) enabled us to identify that ∼10 min/day of PA >700 mg (equivalent to running ∼10 km/h) was positively associated with pQCT-derived measures of bone density, geometry and strength in 11-12-year-olds.

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

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

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

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

Evaluating machine learning algorithms to predict lameness in dairy cattle.

Dairy cattle lameness represents one of the common concerns in intensive and commercial dairy farms. Lameness is characterized by gait-related behavioral changes in cows and multiple approaches are being utilized to associate these changes with lameness conditions including data from accelerometers, and other precision technologies. The objective was to evaluate the use of machine learning algorithms for the identification of lameness conditions in dairy cattle. In this study, 310 multiparous Holstein dairy cows from a herd in Northern Colorado were affixed with a leg-based accelerometer (Icerobotics® Inc, Edinburg, Scotland) to obtain the lying time (min/d), daily steps count (n/d), and daily change (n/d). Subsequently, study cows were monitored for 4 months and cows submitted for claw trimming (CT) were differentiated as receiving corrective claw trimming (CCT) or as being diagnosed with a lameness disorder and consequent therapeutic claw trimming (TCT) by a certified hoof trimmer. Cows not submitted to CT were considered healthy controls. A median filter was applied to smoothen the data by reducing inherent variability. Three different machine learning (ML) models were defined to fit each algorithm which included the conventional features (containing daily lying, daily steps, and daily change derived from the accelerometer), slope features (containing features extracted from each variable in Conventional feature), or all features (3 simple features and 3 slope features). Random forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Time series (ROCKET) were used as ML predictive approaches. For the classification of cows requiring CCT and TCT, ROCKET classifier performed better with accuracy (> 90%), ROC-AUC (> 74%), and F1 score (> 0.61) as compared to other algorithms. Slope features derived in this study increased the efficiency of algorithms as the better-performing models included All features explored. However, further classification of diseases into infectious and non-infectious events was not effective because none of the algorithms presented satisfactory model accuracy parameters. For the classification of observed cow locomotion scores into severely lame and moderately lame conditions, the ROCKET classifier demonstrated satisfactory accuracy (> 0.85), ROC-AUC (> 0.68), and F1 scores (> 0.44). We conclude that ML models using accelerometer data are helpful in the identification of lameness in cows but need further research to increase the granularity and accuracy of classification.

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

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

Anatomical Registration of Implanted Sensors Improves Accuracy of Trunk Tilt Estimates with a Networked Neuroprosthesis.

For individuals with spinal cord injuries (SCIs) above the midthoracic level, a common complication is the partial or complete loss of trunk stability in the seated position. Functional neuromuscular stimulation (FNS) can restore seated posture and other motor functions after paralysis by applying small electrical currents to the peripheral motor nerves. In particular, the Networked Neuroprosthesis (NNP) is a fully implanted, modular FNS system that is also capable of capturing information from embedded accelerometers for measuring trunk tilt for feedback control of stimulation. The NNP modules containing the accelerometers are located in the body based on surgical constraints. As such, their exact orientations are generally unknown and cannot be easily assessed. In this study, a method for estimating trunk tilt that employed the Gram-Schmidt method to reorient acceleration signals to the anatomical axes of the body was developed and deployed in individuals with SCI using the implanted NNP system. An anatomically realistic model of a human trunk and five accelerometer sensors was developed to verify the accuracy of the reorientation algorithm. Correlation coefficients and root mean square errors (RMSEs) were calculated to compare target trunk tilt estimates and tilt estimates derived from simulated accelerometer signals under a variety of conditions. Simulated trunk tilt estimates with correlation coefficients above 0.92 and RMSEs below 5° were achieved. The algorithm was then applied to accelerometer signals from implanted sensors installed in three NNP recipients. Error analysis was performed by comparing the correlation coefficients and RMSEs derived from trunk tilt estimates calculated from implanted sensor signals to those calculated via motion capture data, which served as the gold standard. NNP-derived trunk tilt estimates exhibited correlation coefficients between 0.80 and 0.95 and RMSEs below 13° for both pitch and roll in most cases. These findings suggest that the algorithm is effective at estimating trunk tilt with the implanted sensors of the NNP system, which implies that the method may be appropriate for extracting feedback signals for control systems for seated stability with NNP technology for individuals who have reduced control of their trunk due to paralysis.

Associations between fundamental movement skills and accelerometer-measured physical activity in Chinese children: the mediating role of cardiorespiratory fitness.

Background and purpose: The associations of fundamental motor skills (FMS), health-related physical fitness (e.g., cardiorespiratory fitness, CRF), and moderate-vigorous physical activity (MVPA) have been demonstrated in Western children, but these associations have not yet been validated in a sample of Chinese children. The aims of this study, therefore, were to examine the association between FMS subdomains and MVPA in a sample of Chinese children and to evaluate whether this association is mediated by CRF. Methods: A cross-sectional study consisting of 311 children aged 8-12 years (49.2% girls; mean age = 9.9 years) from Shanghai was conducted. FMS, CRF and MVPA were assessed using the Test of Gross Motor Development-3rd Edition, Progressive Aerobic Cardiovascular Endurance Run and ActiGraph GT3X accelerometers. Preacher & Hayes's bootstrap method was used to test the mediating effects of CRF on the association between FMS and MVPA. Results: CRF fully mediated the association between total FMS and MVPA in girls (indirect effects, b = 0.21, 95% CI [0.07-0.37]), while the mediation was only partial in boys (indirect effects, b = 0.12, 95% CI [0.01-0.26]). CRF fully mediated the association between locomotor skills and MVPA in girls (indirect effects, b = 0.27, 95% CI [0.09- 0.51]), whereas CRF partially mediated the association between object control skills and MVPA in boys (indirect effects, b = 0.15, 95% CI [0.18-0.35]). Conclusion: In order to better design and implement sex-specific interventions aiming to increase MVPA, it is essential to consider FMS subdomains and CRF alongside the sex differences in the association between them.