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Background: The ActiPASS software was developed from the open-source Acti4 activity classification algorithm for thigh-worn accelerometry. However, the original algorithm has not been validated in children or compared with a child-specific set of algorithm thresholds. This study aims to evaluate the accuracy of ActiPASS in classifying activity types in children using 2 published sets of Acti4 thresholds. Methods: Laboratory and free-living data from 2 previous studies were used. The laboratory condition included 41 school-aged children (11.0 [4.8] y; 46.5% male), and the free-living condition included 15 children (10.0 [2.6] y; 66.6% male). Participants wore a single accelerometer on the dominant thigh, and annotated video recordings were used as a reference. Postures and activity types were classified with ActiPASS using the original adult thresholds and a child-specific set of thresholds. Results: Using the original adult thresholds, the mean balanced accuracy (95% CI) for the laboratory condition ranged from 0.62 (0.56–0.67) for lying to 0.97 (0.94–0.99) for running. For the free-living condition, accuracy ranged from 0.61 (0.48–0.75) for lying to 0.96 (0.92–0.99) for cycling. Mean balanced accuracy for overall sedentary behavior (sitting and lying) was ≥0.97 (0.95–0.99) across all thresholds and conditions. No meaningful differences were found between the 2 sets of thresholds, except for superior balanced accuracy of the adult thresholds for walking under laboratory conditions. Conclusions: The results indicate that ActiPASS can accurately classify different basic types of physical activity and sedentary behavior in children using thigh-worn accelerometer data.

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.

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.

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.

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.

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland., Funding AgencyNational Health and Medical Research Council of Australia (Grant Number2020-APP1180812)Uppsala UniversityRegional research funds ALF Uppsala Academic Hospital

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.

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 assoc

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.

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 < 10% were indicative of good validity. Comparison of AM attachment positions was completed using Spearman Rho correlation coefficients and Meng's tests. RESULTS: Criterion validity of the AM (frontolateral thigh) was good (average relative time differences: 0.25% for sitting, 4.69% for standing, 2.46% for walking, 1.96% for upright activity, 3.19% for cycling), except for running (34.6%). Spearman Rho correlation coefficients were greater between video/frontolateral thigh position than video/frontal thigh position and video/pant pocket position for body posture and movement categories sitting, standing, walking, and upright activity (p < 0.01 for all). CONCLUSIONS: The AM, positioned on the frontolateral thigh, demonstrated good criterion validity in ambulatory adults with CP. Though the Activ8 offers potential as an objective measure of physical activity, appropriate positioning is paramount for valid measure

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.

Background: To assess physical behaviour, including physical activity and sedentary behaviour, of ambulatory adolescents and young adults with cerebral palsy (CP). We compared participant physical behaviour to that of able-bodied persons and assessed differences related to Gross Motor Functioning Classification System (GMFCS) level and CP distribution (unilateral/bilateral). Methods. In 48 ambulatory persons aged 16 to 24 years with spastic CP and in 32 able-bodied controls, physical behaviour was objectively determined with an accelerometer-based activity monitor. Total duration, intensity and type of physical activity were assessed and sedentary time was determined (lying and sitting). Furthermore, distribution of walking bouts and sitting bouts was specified. Results: Adolescents and young adults with CP spent 8.6% of 24 hours physically active and 79.5% sedentary, corresponding with respectively 123 minutes and 1147 minutes per 24 hours. Compared to able-bodied