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Abstract 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.

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

Background Sensor technology could provide solutions to monitor postures and motions and to help hospital patients reach their rehabilitation goals with minimal supervision. Synthesized information on device applications and methodology is lacking.Objectives The purpose of this scoping review was to provide an overview of device applications and methodological approaches to monitor postures and motions in hospitalized patients using sensor technology.Methods A systematic search of Embase, Medline, Web of Science and Google Scholar was completed in February 2023 and updated in March 2024. Included studies described populations of hospitalized adults with short admission periods and interventions that use sensor technology to objectively monitor postures and motions. Study selection was performed by two authors independently of each other. Data extraction and narrative analysis focused on the applications and methodological approaches of included articles using a personalized standard form to extract information on device, measurement and analysis characteristics of included studies and analyse frequencies and usage.Results A total of 15.032 articles were found and 49 articles met the inclusion criteria. Devices were most often applied in older adults (n = 14), patients awaiting or after surgery (n = 14), and stroke (n = 6). The main goals were gaining insight into patient physical behavioural patterns (n = 19) and investigating physical behaviour in relation to other parameters such as muscle strength or hospital length of stay (n = 18). The studies had heterogeneous study designs and lacked completeness in reporting on device settings, data analysis, and algorithms. Information on device settings, data analysis, and algorithms was poorly reported.Conclusions Studies on monitoring postures and motions are heterogeneous in their population, applications and methodological approaches. More uniformity and transparency in methodology and study reporting would improve reproducibility, interpretation and generalization of results. Clear guidelines for reporting and the collection and sharing of raw data would benefit the field by enabling study comparison and reproduction.

Abstract Background The longitudinal associations between physical behaviours and lean muscle mass indices need to be better understood to aid healthy ageing intervention development. Methods We assessed physical behaviours (total physical activity, moderate-to-vigorous physical activity (MVPA), light physical activity, total sedentary time and prolonged sedentary bout time) for 7 days using hip-worn accelerometers. We also assessed domain-specific physical behaviours (walking, cycling, gardening and housework time) with self-report questionnaires at baseline (2006–2011) and follow-up (2012–2016) in the European Prospective Investigation into Cancer (EPIC)-Norfolk study. We assessed body composition using dual-energy X-ray absorptiometry (DEXA) at follow-up in 1535 participants (≥ 60 years at baseline). From this, we derived appendicular lean muscle mass (ALM) indices (% relative ALM = (ALM/total body weight)*100), body mass index (BMI)-scaled ALM (ALM/BMI, kg/kg/m2) and height-scaled ALM (ALM/height2, kg/m2)). We evaluated the prospective associations of both baseline and change in physical behaviours with follow-up muscle mass indices using multivariable linear regression. Results Over 5.5 years (SD 14.8) follow-up, higher baseline accelerometer-measured physical activity and lower sedentary time were associated with higher subsequent relative ALM and BMI-scaled ALM, but not height-scaled ALM (e.g. 0.02% higher subsequent relative ALM per minute/day of baseline MVPA for men). Greater increases in physical activity and greater declines in sedentary time variables were associated with higher subsequent relative ALM and BMI-scaled ALM, but not height-scaled ALM (e.g. 0.001 kg/kg/m2 subsequent BMI-scaled ALM and 0.04% subsequent relative ALM per min/day/year increases in LPA over follow-up; 0.001 kg/kg/m2 subsequent BMI-scaled ALM and -0.03% subsequent relative ALM per min/day/year less of total sedentary time over follow-up). Greater increases in women’s cycling and gardening over follow-up were associated with greater subsequent relative ALM (cycling 0.9% per hour/week/year; gardening 0.2% per hour/week/year) and BMI-scaled ALM (cycling 0.03 kg/kg/m2 per hour/week/year; gardening 0.004 kg/kg/m2 per hour/week/year). Conclusion Physical behaviours across all intensities, and in women more specifically cycling and gardening, may help prevent age-related declines in muscle mass.

Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor placement on the accuracy of detecting SB and PA in older adults, as well as a novel random forest algorithm trained on data from older persons. Four monitor types (ActiGraph wGT3X-BT, ActivPAL3c VT, GENEActiv Original, and DynaPort MM+) were simultaneously worn on five anatomical sites during ten different activities by a sample of twenty older adults (70.0 (12.0) years; 10 women). The results indicated that collecting metabolic equivalent (MET) data for 60 s provided the most representative results, minimising variability. In addition, thigh-worn monitors, including ActivPAL, Random Forest, and Sedentary Sphere—Thigh, exhibited superior performance in classifying SB, with balanced accuracies ≥ 94.2%. Other monitors, such as ActiGraph, DynaPort MM+, and GENEActiv Sedentary Sphere—Wrist, demonstrated lower performance. ActivPAL and GENEActiv Random Forest outperformed other monitors in participant-specific balanced accuracies for SB classification. Only thigh-worn monitors achieved acceptable overall balanced accuracies (≥80.0%) for SB, standing, and medium-to-vigorous PA classifications. In conclusion, it is advisable to position accelerometers on the thigh, collect MET data for ≥60 s, and ideally utilise population-specific trained algorithms. [ABSTRACT FROM AUTHOR]

Abstract Background Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours. Methods The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss. Results 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent. Conclusion Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor placement on the accuracy of detecting SB and PA in older adults, as well as a novel random forest algorithm trained on data from older persons. Four monitor types (ActiGraph wGT3X-BT, ActivPAL3c VT, GENEActiv Original, and DynaPort MM+) were simultaneously worn on five anatomical sites during ten different activities by a sample of twenty older adults (70.0 (12.0) years; 10 women). The results indicated that collecting metabolic equivalent (MET) data for 60 s provided the most representative results, minimising variability. In addition, thigh-worn monitors, including ActivPAL, Random Forest, and Sedentary Sphere—Thigh, exhibited superior performance in classifying SB, with balanced accuracies ≥ 94.2%. Other monitors, such as ActiGraph, DynaPort MM+, and GENEActiv Sedentary Sphere—Wrist, demonstrated lower performance. ActivPAL and GENEActiv Random Forest outperformed other monitors in participant-specific balanced accuracies for SB classification. Only thigh-worn monitors achieved acceptable overall balanced accuracies (≥80.0%) for SB, standing, and medium-to-vigorous PA classifications. In conclusion, it is advisable to position accelerometers on the thigh, collect MET data for ≥60 s, and ideally utilise population-specific trained algorithms.

Background: Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours. Methods: The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss. Results: 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent. Conclusion: Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465). [ABSTRACT FROM AUTHOR]

Although the independent within-person association between physical activity and sedentary behaviour with valence in daily life has been extensively studied, few studies have used compositional data analysis to examine how different movement behaviour compositions are related to valence in daily life. This study aimed to examine the within-person association between wake-time movement behaviour compositions with affective valence and the extent to which replacing time spent sedentary with physical activity was associated with valence within individuals in daily life. A 7-day ecological momentary study design was used whereby 94 Canadian university students (M age = 19.45, SD = 2.21, 78.7 % female) reported on affective valence using an adapted version of the Feeling Scale at 7 randomly timed prompts each day. In addition, activPAL accelerometers were worn continuously by participants on their right thigh for the duration of the study to determine time spent engaging in sedentary behaviours and physical activity. Compositional data analysis with isotemporal substitution models were used to examine the within-person association between movement behaviour compositions and affective valence. The within-person association between movement behaviours and affective valence was weak (r2 = 0.013). Nevertheless, engaging in less sedentary time than usual and instead engaging in physical activity was significantly related to more positive affective valence. Considering light intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) separately, replacing time spent sedentary with time engaged in MVPA and LPA both had a significant positive association on affective valence, although the association with MVPA was stronger than the association with LPA. The results provide unique insights into how replacing sedentary time with physical activity in daily life, especially MVPA, may be associated with more feelings of pleasure. These results may be useful to help inform the development of just-in-time adaptive interventions. • Replacing time sedentary with physical activity related to positive valence. • Replacing sedentary time with MVPA has stronger association with valence than LPA. • Breaking up sedentary time with physical activity may be an effective strategy to promote positive affect. [ABSTRACT FROM AUTHOR]

Background: Sensor technology could provide solutions to monitor postures and motions and to help hospital patients reach their rehabilitation goals with minimal supervision. Synthesized information on device applications and methodology is lacking., Objectives: The purpose of this scoping review was to provide an overview of device applications and methodological approaches to monitor postures and motions in hospitalized patients using sensor technology., Methods: A systematic search of Embase, Medline, Web of Science and Google Scholar was completed in February 2023 and updated in March 2024. Included studies described populations of hospitalized adults with short admission periods and interventions that use sensor technology to objectively monitor postures and motions. Study selection was performed by two authors independently of each other. Data extraction and narrative analysis focused on the applications and methodological approaches of included articles using a personalized standard form to extract information on device, measurement and analysis characteristics of included studies and analyse frequencies and usage., Results: A total of 15.032 articles were found and 49 articles met the inclusion criteria. Devices were most often applied in older adults ( n  = 14), patients awaiting or after surgery ( n  = 14), and stroke ( n  = 6). The main goals were gaining insight into patient physical behavioural patterns ( n  = 19) and investigating physical behaviour in relation to other parameters such as muscle strength or hospital length of stay ( n  = 18). The studies had heterogeneous study designs and lacked completeness in reporting on device settings, data analysis, and algorithms. Information on device settings, data analysis, and algorithms was poorly reported., Conclusions: Studies on monitoring postures and motions are heterogeneous in their population, applications and methodological approaches. More uniformity and transparency in methodology and study reporting would improve reproducibility, interpretation and generalization of results. Clear guidelines for reporting and the collection and sharing of raw data would benefit the field by enabling study comparison and reproduction.

To evaluate the effects of a combination of wheelchair mobility skills (WMS) training and exercise training on physical activity (PA), WMS, confidence in wheelchair mobility, and physical fitness. Youth using a manual wheelchair (n = 60) participated in this practice-based intervention, with a waiting list period (16 weeks), exercise training (8 weeks), WMS training (8 weeks), and follow-up (16 weeks). Repeated measures included: PA (Activ8), WMS (Utrecht Pediatric Wheelchair Mobility Skills Test), confidence in wheelchair mobility (Wheelchair Mobility Confidence Scale), and physical fitness (cardiorespiratory fitness, (an)aerobic performance) and were analysed per outcome parameter using a multilevel model analyses. Differences between the waiting list and training period were determined with an unpaired sample t-test. Multilevel model analysis showed significant positive effects for PA (p = 0.01), WMS (p < 0.001), confidence in wheelchair mobility (p < 0.001), aerobic (p < 0.001), and anaerobic performance (p < 0.001). Unpaired sample t-tests underscored these effects for PA (p < 0.01) and WMS (p < 0.001). There were no effects on cardiorespiratory fitness. The order of training (exercise before WMS) had a significant effect on confidence in wheelchair mobility. A combination of exercise and WMS training appears to have significant positive long-term effects on PA, WMS, confidence in wheelchair mobility, and (an)aerobic performance in youth using a manual wheelchair. Exercise training and wheelchair mobility skills (WMS) training can lead to a sustained improvement in physical activity (PA) in youth using a manual wheelchair. These combined trainings can also lead to a sustained increase in WMS, confidence in wheelchair mobility, and (an)aerobic performance. More attention is needed in clinical practice and in research towards improving PA in youth using a manual wheelchair. [ABSTRACT FROM AUTHOR]

Abstract Background Accurate measurement of physical behaviour is paramount to better understand lifestyle, health, and functioning, particularly in adults with physical disability as they may be at higher risk of sedentary lifestyle and subsequent negative health consequences. This study aimed: 1) to evaluate the criterion validity of a novel and clinically applicable activity monitor (AM, Activ8), in the detection of body postures and movements in adults with spastic cerebral palsy (CP); and 2) to evaluate the extent that the AM’s positioning affects validity. Methods In this cross-sectional study, 14 ambulatory adults with CP [9 men; mean (SD) age, 35.4 (13.1) years] performed standardized activities while wearing three Activ8 monitors - frontolateral thigh (primary position), frontal thigh, and pant pocket - and being video recorded (criterion measure). AM activity output was compared to synchronized video recordings. Absolute (seconds) and relative [(video time–AM time)/mean time, %] time differences between methods were calculated. Relative time differences of

This article is intended to investigate the advection-diffusion process using a difference-scheme based Monte Carlo simulation approach. This is accomplished while walking randomly in the solution region, and then the solution is recorded at a point at a certain time at the end of each random walk (RW). Therefore, the advantage of this technique is to use a more realistic approach based on probability of solving the model equation defining the system. In order to accurately analyse environmental responses, the computed solutions are discussed in detail. The proposed technique has been found to be effective in capturing the behaviour of the more involved processes. [ABSTRACT FROM AUTHOR]

Objectives Previous research has shown strong associations between occupational physical activity (OPA) and need for recovery (NFR). However this research has only utilized self-reported measures of OPA which may be biased. Thus, there is a need for investigating if the previously documented association between self-reported OPA and NFR can be found when using technical measures of OPA. There is also the need to investigate whether older workers are particularly susceptible to increased NFR, since age-related declines in physical capacity mean that it is likely these workers will have a higher NFR for a given physical activity. The aim of this study was to investigate the association between technically measured OPA and NFR, and whether this relationship is modified by age. Methods This study utilized data from the Danish Physical Activity Cohort with Objective Measurements cohort—comprising Danish workers (n = 840) from the cleaning, manufacturing, and transportation sectors. OPA was measured by accelerometers attached to the thigh and upper back for at least one work day and classified into four physical behaviour categories (sedentary, standing, light, or moderate/vigorous). NFR was measured using a shortened version of the Danish NFR scale. Analysis was conducted using linear regression and isotemporal substitution analyses for compositional data. Results The overall association between OPA and NFR was statistically significant in the unadjusted model (P < 0.001), but not when adjusted for age, sex, occupation, and shift work (P = 0.166). Isotemporal substitution showed small but significant reductions in NFR when increasing sedentary time relative to other behaviours (adjusted: ΔNFR = −0.010 [−0.019; −0.001]). There were no significant interactions between age and OPA (P = 0.409). Conclusions This study found significant associations between OPA and NFR, but the effect sizes were small. Reallocating 30 min to sedentary behaviours from other behaviours was associated with a reduced NFR, but the effect size may not be practically relevant. Moreover, no clear modifying effects of age were identified. [ABSTRACT FROM AUTHOR]

Background: Different interventions are offered to children with cerebral palsy (CP) to improve the activity domain of the international classification of functioning (ICF). In therapy settings, the focus is mostly on motor capacity, but the ultimate goal is to improve motor performance. We therefore examined if changes in motor capacity outcomes are accompanied by changes in objectively measured motor performance after a 3‐month intensive treatment period in ambulatory children with CP. Methods: A secondary analysis on prospective clinical trial data was performed using multivariate linear regression. Sixty‐five children (37 boys and 28 girls) with spastic CP, mean age 7 years and 3 months, Gross Motor Function Classification System (GMFCS) levels I–III were involved in a distinct 3‐month intensive treatment period. Motor capacity (Gross Motor Function Measure [GMFM], functional muscle strength [FMS], and walking speed [WS]) and motor performance (using three Actigraph‐GT3X+‐derived outcome measures) were measured at baseline, 12 and 24 weeks. Results: No significant associations were found for any of the change scores (∆12) between motor capacity and motor performance after a 12‐week intensive treatment period. After 24 weeks, ∆24FMS (p =.042) and ∆24WS (p =.036) were significantly associated with changes in motor performance outcome measure percentage of time spent sedentary (∆24%sedentary). In this model, 16% of variance of ∆24%sedentary was explained by changes in motor capacity (p =.030). Conclusions: Changes in motor capacity are mostly not accompanied by changes in objectively measured motor performance after an intensive treatment period for ambulatory children with CP. These findings should be taken into account during goal setting and are important to manage expectations of both short‐ and longer term effects of treatment programmes. [ABSTRACT FROM AUTHOR]

Abstract Background There is some evidence to suggest that dog ownership may improve physical activity (PA) among older adults, but to date, studies examining this, have either depended on self-report or incomplete datasets due to the type of activity monitor used to record physical activity. Additionally, the effect of dog ownership on sedentary behaviour (SB) has not been explored. The aim of the current study was to address these issues by using activPAL monitors to evaluate the influence of dog ownership on health enhancing PA and SB in a longitudinal study of independently-mobile, community-dwelling older adults. Methods Study participants (43 pairs of dog owners and non-dog owners, matched on a range of demographic variables) wore an activPAL monitor continuously for three, one-week data collection periods over the course of a year. Participants also reported information about their own and their dog demographics, caring responsibilities, and completed a diary of wake times. Diary data was used to isolate waking times, and outcome measures of time spent walking, time spent walking at a moderate cadence (>100 steps/min), time spent standing, time spent sitting, number of sitting events (continuous periods of sitting), and the number of and of time spent sitting in prolonged events (>30 min). For each measure, a linear mixed effects model with dog ownership as a fixed effect, and a random effects structure of measurement point nested in participant nested in pair was used to assess the effect of dog ownership. Results Owning a dog indicated a large, potentially health improving, average effect of 22 min additional time spent walking, 95%CI (12, 34), and 2760 additional steps per day, 95%CI (1667, 3991), with this additional walking undertaken at a moderate intensity cadence. Dog owners had significantly fewer sitting events. However, there were no significant differences between the groups for either the total time spent sitting, or the number or duration of prolonged sedentary events. Conclusions The scale of the influence of dog ownership on PA found in this study, indicates that future research regarding PA in older adults should assess and report dog ownership and/or dog walking status.

Background: Studies have seldom used Compositional Data Analysis (CoDA) to map the effects of sleep, sedentary behaviour, and physical activity on older adults' cardio-metabolic profiles. This study therefore aimed to illustrate how sleep, sedentary behaviour, and physical activity profiles differ between older adult groups (60–89 years), with 'low' compared to those with 'high' concentrations of endocrine cardio-metabolic disease risk markers, using CoDA. Method: Ninety-three participants (55% female) wore a thigh-mounted triaxial accelerometer for seven consecutive free-living days. Accelerometer estimates of daily average hours of engagement in sedentary behaviour (SB), standing, light-intensity physical activity (LIPA), sporadic moderate-vigorous physical activity (sMVPA, accumulated with bouts between 1 and 10 min), 10-min moderate-vigorous physical activity (10MVPA, accumulated with bouts ≥10 min), in addition to self-reported sleeping hours were reported. Fasted whole blood concentrations of total cholesterol, triglyceride, glucose, and glycated haemoglobin, and serum lipoprotein lipase (LPL), interleukin-6 (IL-6), and procollagen III N-terminal propeptide were determined. Results: Triglyceride concentration appeared to be highly dependent on 10MVPA engagement as the 'low' and 'high' concentration groups engaged in 48% more and 32% less 10MVPA, respectively, relative to the geometric mean of the entire study sample. Time-use composition of the 'low' LPL group's engagement in 10MVPA was 26% less, while the 'high' LPL group was 7.9% more, than the entire study sample. Time-use composition of the 'high' glucose and glycated haemoglobin groups appeared to be similar as both engaged in more Sleep and SB, and less 10MVPA compared to the study sample. Participants with a 'low' IL-6 concentration engaged in 4.8% more Sleep and 2.7% less 10MVPA than the entire study sample. Time-use composition of the Total Cholesterol groups was mixed with the 'low' concentration group engaging in more Standing and 10MVPA but less Sleep, SB, LIPA, and sMVPA than the entire study sample. Conclusion: Older adults should aim to increase 10MVPA engagement to improve lipid profile and decrease SB engagement to improve glucose profile. [ABSTRACT FROM AUTHOR]

Purpose: Standing up, sitting down and walking require considerable effort and coordination, which are crucial indicators to rehabilitation (e.g. stroke), and in older populations may indicate the onset of frailty and physical and cognitive decline. Currently, there are few reports robustly quantifying sit-to-stand and stand-to-sit transitions in free-living environments. The aim of this study was to identify and quantify these transitions using the peak velocity of sit-to-stand and stand-to-sit transitions to determine if these velocities were different in a healthy cohort and a mobility-impaired population.Methods: Free-living sit-to-stand and stand-to-sit acceleration data were recorded from 21 healthy volunteers and 34 stroke survivors using activPAL3™ monitors over a one-week period. Thigh inclination velocity was calculated from these accelerometer data. Maximum velocities were compared between populations.Results: A total of 10,299 and 11,392 sit-to-stand and stand-to-sit transitions were recorded in healthy volunteers and stroke survivors, respectively. Healthy volunteers had significantly higher overall mean peak velocities for both transitions compared with stroke survivors [70.7°/s ± 52.2 versus 44.2°/s ± 28.0 for sit-to-stand, P < 0.001 and 74.7°/s ± 51.8 versus 46.0°/s ± 31.9 for stand-to-sit; P < 0.001]. Mean peak velocity of transition was associated with increased variation in peak velocity across both groups.Conclusion: There were significant differences in the mean peak velocity of sit-to-stand and stand-to-sit transitions between the groups. Variation in an individual's mean peak velocity may be associated with the ability to perform these transitions. This method could be used to evaluate the effectiveness of interventions following injury such as stroke, as well as monitor decline in functional ability. [ABSTRACT FROM AUTHOR]

One way to represent the physical behaviour of the transformer winding under high frequencies is to construct an equivalent circuit using modernised methods. This study represents a luxurious and time‐efficient computation method based on particle swarm optimisation with simulated annealing (PSO‐SA) method to construct an equivalent circuit of a transformer winding, from its frequency response analysis. The proposed method uses a simulated annealing method as a local search tool to improve the convergence and the characteristics of particle swarm optimisation in fast calculation. Furthermore, a comparative study of PSO‐SA, crow search algorithm and genetic algorithm method is made. Different case studies of a transformer winding with an air core are considered. According to the results, the PSO‐SA has higher efficiency and faster convergence. [ABSTRACT FROM AUTHOR]

Background: Rett syndrome (RTT) is a rare neurodevelopmental disorder leading to multiple disabilities and high dependency on caregivers. This study aimed to: (1) describe the patterns of sedentary time and daily steps and (2) identify the association of individual and environmental characteristics with sedentary time. Methods: All Danish females with RTT older than 5 years of age and with a MECP2 mutation were invited to participate. The activPAL and StepWatch Activity Monitor (SAM) were worn by participants for at least four days. Sedentary time and step counts were plotted by time to examine daily activity patterns. Associations between sedentary time and individual and environmental covariates were assessed with linear regression models. Results: The median (interquartile range) age of participants was 22.0 (14.3-36.5) years. On average 83.3% (standard deviation 13.9%) of waking hours were spent in sedentary behaviours (n = 48) and the median (interquartile range) daily step count was 5128 (2829-7704) (n = 28). Females older than 33.5 years, and those unable to walk independently were more sedentary. Conclusions: This study demonstrated high levels of sedentary time and low daily step counts in a Danish population of females with RTT. Advancing age and lower walking skills were associated with higher levels of sedentary time. Implications for Rehabilitation: Sedentary lifestyles in individuals with disabilities have a negative impact on health and quality of life. High levels of sedentary time and low daily step counts were demonstrated in a Danish population of females with Rett syndrome. Advancing age and inability to walk independently were strongly associated with higher levels of sedentary time in females with Rett syndrome. Understanding patterns of sedentary behaviour and physical activity can aid health care professionals in developing health-promoting physical activity interventions. [ABSTRACT FROM AUTHOR]

Body postural allocation during daily life is important for health, and can be assessed with thigh-worn accelerometers. An algorithm based on sedentary bouts from the proprietary ActivePAL software can detect lying down from a single thigh-worn accelerometer using rotations of the thigh. However, it is not usable across brands of accelerometers. This algorithm has the potential to be refined. Aim: To refine and assess the validity of an algorithm to detect lying down from raw data of thigh-worn accelerometers. Axivity-AX3 accelerometers were placed on the thigh and upper back (reference) on adults in a development dataset (n = 50) and a validation dataset (n = 47) for 7 days. Sedentary time from the open Acti4-algorithm was used as input to the algorithm. In addition to the thigh-rotation criterion in the existing algorithm, two criteria based on standard deviation of acceleration and a time duration criterion of sedentary bouts were added. The mean difference (95% agreement-limits) between the total identified lying time/day, between the refined algorithm and the reference was +2.9 (−135,141) min in the development dataset and +6.5 (−145,159) min in the validation dataset. The refined algorithm can be used to estimate lying time in studies using different accelerometer brands.

Physical activity (PA) and sedentary behaviour (SB) are important components of physical behaviour associated with long-term health outcomes. Environmental and cultural factors may influence physical behaviour. To explore full day PA and SB in children and adolescents (2-18 years old) in the Middle East, a systematic literature review was performed including 183 journal articles. A wide range of PA and SB outcomes were reported, in some cases making synthesis of results difficult. As a consequence, results were generally reported narratively (MVPA time, total PA, SB time). Meta-regression of daily step count revealed females took 4600 fewer steps than males, with 3000 fewer steps on weekdays than weekends, and overweight individuals taking 2800 fewer steps/day. Steps decreased with age. Meta-regression for TV viewing time demonstrated an increase by 0.04 h per year of age. Even though environmental and cultural conditions may be different, PA and SB of children and adolescents in the Middle East were largely comparable to those of Europeans and North Americans. The wide range of data collection instruments used (both self-report questionnaire and body-worn devices) and heterogeneity of data made synthesis of reported data across studies very difficult, suggesting a need for greater standardisation of data collection methods.

Recent technological advances have enabled the continuous and unobtrusive monitoring of human behaviour. However, most of the existing studies focus on detecting human behaviour under the limitation of one behavioural aspect, such as physical behaviour and not addressing human behaviour in a broad sense. For this reason, we propose a novel framework that will serve as the principal generator of knowledge on the user’s behaviour. The proposed framework moves beyond the current trends in automatic behaviour analysis by detecting and inferring human behaviour automatically, based on multimodal sensor data. In particular, the framework analyses human behaviour in a holistic approach, focusing on different behavioural aspects at the same time; namely physical, social, emotional and cognitive behaviour. Furthermore, the suggested framework investigates user’s behaviour over different periods, introducing the concept of short-term and long-term behaviours and how these change over time.

Background: There is some evidence to suggest that dog ownership may improve physical activity (PA) among older adults, but to date, studies examining this, have either depended on self-report or incomplete datasets due to the type of activity monitor used to record physical activity. Additionally, the effect of dog ownership on sedentary behaviour (SB) has not been explored. The aim of the current study was to address these issues by using activPAL monitors to evaluate the influence of dog ownership on health enhancing PA and SB in a longitudinal study of independently-mobile, community-dwelling older adults.Methods: Study participants (43 pairs of dog owners and non-dog owners, matched on a range of demographic variables) wore an activPAL monitor continuously for three, one-week data collection periods over the course of a year. Participants also reported information about their own and their dog demographics, caring responsibilities, and completed a diary of wake times. Diary data was used to isolate waking times, and outcome measures of time spent walking, time spent walking at a moderate cadence (>100 steps/min), time spent standing, time spent sitting, number of sitting events (continuous periods of sitting), and the number of and of time spent sitting in prolonged events (>30 min). For each measure, a linear mixed effects model with dog ownership as a fixed effect, and a random effects structure of measurement point nested in participant nested in pair was used to assess the effect of dog ownership.Results: Owning a dog indicated a large, potentially health improving, average effect of 22 min additional time spent walking, 95%CI (12, 34), and 2760 additional steps per day, 95%CI (1667, 3991), with this additional walking undertaken at a moderate intensity cadence. Dog owners had significantly fewer sitting events. However, there were no significant differences between the groups for either the total time spent sitting, or the number or duration of prolonged sedentary events.Conclusions: The scale of the influence of dog ownership on PA found in this study, indicates that future research regarding PA in older adults should assess and report dog ownership and/or dog walking status. [ABSTRACT FROM AUTHOR]

Human behaviour analysis through smartphone devices has been an active field for more than a decade and there are still a lot of key aspects to be addressed. This paper surveys the state-of-the-art in human behaviour analysis based on smartphones. We categorise prior works into four main sensing modalities related to physical, cognitive, emotional and social behaviour. Finally, we conclude with the outcomes of this survey and we illustrate our ideas for future research in the area of human behaviour understanding.