Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children

Accelerometer techniques for capturing human movement validated against direct observation: a scoping review

Objective.Accelerometers are devices commonly used to measure human physical activity and sedentary time. Accelerometer capabilities and analytical techniques have evolved rapidly, making it difficult for researchers to keep track of advances and best practices for data processing and analysis. The objective of this scoping review is to determine the existing methods for analyzing accelerometer data for capturing human movement which have been validated against the criterion measure of direct observation.Approach.This scoping review searched 14 academic and 5 grey databases. Two independent raters screened by title and abstract, then full text. Data were extracted using Microsoft Excel and checked by an independent reviewer.Mainresults.The search yielded 1039 papers and the final analysis included 115 papers. A total of 71 unique accelerometer models were used across a total of 4217 participants. While all studies underwent validation from direct observation, most direct observation occurred live (55%) or using recordings (42%). Analysis techniques included machine learning (ML) approaches (22%), the use of existing cut-points (18%), receiver operating characteristic curves to determine cut-points (14%), and other strategies including regressions and non-ML algorithms (8%).Significance.ML techniques are becoming more prevalent and are often used for activity identification. Cut-point methods are still frequently used. Activity intensity is the most assessed activity outcome; however, both the analyses and outcomes assessed vary by wear location. This scoping review provides a comprehensive overview of accelerometer analysis and validation techniques using direct observation and is a useful tool for researchers using accelerometers.

Associations between physical activity, sedentary time, and neurocognitive function during adolescence: Evidence from accelerometry and the flanker P300

Physical activity and sedentary behavior are two lifestyle factors related to overall health during adolescence. Public health efforts emphasize the importance of increasing physical activity to improve physical and mental health outcomes, including neurocognitive functioning. However, the unique effects of sedentary behavior on neurocognitive functioning remain unclear. This study aimed to investigate associations between daily moderate-to-vigorous physical activity (MVPA), sedentary time, and neurocognitive functioning during adolescence. Fifty-seven participants (37% female) between the ages of 13 and 17 years wore an accelerometer on their non-dominant wrist for approximately 1 week to quantify daily MVPA and sedentary time prior to completing a flanker task to elicit P300 amplitude at a laboratory visit. Results indicated that daily MVPA and sedentary time exhibited unique, significant associations with P300 amplitude in opposing directions: increased daily MVPA was correlated with larger P300 amplitudes, while increased daily sedentary time was linked to reduced P300 amplitudes. Notably, these associations remained significant even after adjusting for age, sex, and BMI-for-age percentile. These findings underscore the independent influence of daily MVPA and sedentary time on neurocognitive functioning during adolescence. Future research should explore whether modifying MVPA levels can improve neurocognitive outcomes-including the P300-during adolescence, and determine whether reducing sedentary time results in similar or differential effects.

Calibration and validation of physical behaviour cut-points using wrist-worn ActiGraphs for children and adolescents: A systematic review

OBJECTIVES

To review cut-points calibrated and independently validated from wrist-worn ActiGraph accelerometers to measure moderate to vigorous physical activity (MVPA) and time spent sedentary (SED) in children and adolescents.

DESIGN

Systematic literature review.

METHODS

Five databases were searched for relevant cut-point calibration and independent validation studies relating to wrist worn ActiGraphs in children and adolescents from inception through 30 April 2022. Extracted data included: country of publication; study name; population; device model; wear location; sampling frequency; epoch length; activity protocol; criterion method and definitions used to classify PA intensity; statistical methods for calibration; statistical methods for validation/cross-validation; and MVPA and SED outcome.

RESULTS

Fourteen calibration studies and seven independent validation studies were identified. Calibrated cut-points for MVPA vector magnitude counts ranged from 7065 to 9204 counts per minute (cpm) and 63.5 to 201 milli-gravitational units (mg). For SED, calibrated cut-points ranged from <2556 cpm to 4350 cpm and 30.8 to 48.1 mg. Classification accuracy values determined by independent validation studies varied, with kappa values ranging from 0.31 to 0.60 and area under the curve statistics ranging from 0.51 to 0.84 for MVPA and kappa values ranging from 0.31 to 0.44 and area under the curve statistics ranging from 0.70 to 0.85 for SED.

CONCLUSIONS

The results of this systematic literature review support the use of the Crouter and colleagues cut-points for the measurement of MVPA and SED for children and adolescents aged 6-12 years. Further work is required to independently validate cut-points developed in younger children and older adolescents.

Supervised Learning of Physical Activity Features From Functional Accelerometer Data

OBJECTIVE

We propose a new health informatics framework to analyze physical activity (PA) from accelerometer devices. Accelerometry data enables scientists to extract personal digital features useful for precision health decision making. Existing methods in accelerometry data analysis typically begin with discretizing summary counts by certain fixed cutoffs into activity categories. One well-known limitation is that the chosen cutoffs are often validated under restricted settings, and cannot be generalizable across populations, devices, or studies.

METHODS

We develop a data-driven approach to overcome this bottleneck in PA data analysis, in which we holistically summarize a subject's activity profile using Occupation-Time curves (OTCs), which describe the percentage of time spent at or above a continuum of activity count levels. We develop multi-step adaptive learning algorithms to perform supervised learning via a scalar-on-function model that involves OTC as the functional predictor of interest as well as other scalar covariates. Our learning analytic first incorporates a hybrid approach of fused lasso for clustering and Hidden Markov Model for changepoint detection, then executes refinement procedures to determine activity windows of interest.

RESULTS

We evaluate and illustrate the performance of the proposed learning analytic through simulation experiments and real-world data analyses to assess the influence of PA on biological aging. Our findings indicate a different directional relationship between biological age and PA depending on the specific outcome of interest.

SIGNIFICANCE

Our bioinformatics methodology involves the biomedical outcome of interest to detect different critical points, and is thus adaptive to the specific data, study population, and health outcome under investigation.

Spirometry correlates with physical activity in patients with Duchenne muscular dystrophy

BACKGROUND

Duchenne muscular dystrophy (DMD) is associated with progressive muscle weakness and respiratory decline. To date, studies have focused on respiratory decline and loss of ambulation as a metric of loss of skeletal muscle strength. However, new functional measures can assess skeletal muscle disease regardless of ambulatory status. The relationship between these tests and concurrent lung function is currently unexplored.

OBJECTIVE

To assess the correlation between spirometry measurements and functional muscle assessments such as accelerometry and quantitative muscle testing (QMT).

METHODS

Enrolled patients with DMD underwent accelerometry and QMT at study clinic visits. Any pulmonary function testing within 6 months of visit was obtained from the electronic medical record. The Spearman correlation coefficient was used to assess the relationship between spirometry and functional muscle testing.

RESULTS

Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1 ) demonstrated the strongest correlation with accelerometry. Both FVC and FEV1 showed a similar relationship to accelerometry when activity was divided into intensity categories, with low intensity and moderate-to-vigorous activity categories showing the strongest correlation. Maximal expiratory pressure (MEP) and FVC showed the most robust correlations with total QMT (p < 0.001 and p < 0.01, respectively).

CONCLUSION

Lung function, specifically FVC percent predicted and FEV1 %p, shows a good correlation with upper and lower extremity skeletal muscle functional testing such as accelerometry and QMT.

Evaluation of Wrist Accelerometer Cut-Points for Classifying Physical Activity Intensity in Youth

Background

Wrist worn accelerometers are convenient to wear and provide greater compliance. However, methods to transform the resultant output into predictions of physical activity (PA) intensity have been slow to evolve, with most investigators continuing the practice of applying intensity-based thresholds or cut-points. The current study evaluated the classification accuracy of seven sets of previously published youth-specific cut-points for wrist worn ActiGraph accelerometer data.

Methods

Eighteen children and adolescents [mean age (± SD) 14.6 ± 2.4 years, 10 boys, 8 girls] completed 12 standardized activity trials. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the wrist and energy expenditure (Youth METs) was measured directly using the Oxycon Mobile portable calorimetry system. Seven previously published sets of ActiGraph cut-points were evaluated: Crouter regression vertical axis, Crouter regression vector magnitude, Crouter ROC curve vertical axis, Crouter ROC curve vector magnitude, Chandler ROC curve vertical axis, Chandler ROC curve vector magnitude, and Hildebrand ENMO. Classification accuracy was evaluated via weighted Kappa. Confusion matrices were generated to summarize classification accuracy and identify patterns of misclassification.

Results

The cut-points exhibited only moderate agreement with directly measured PA intensity, with Kappa ranging from 0.45 to 0.58. Although the cut-points classified sedentary behavior accurately (> 95%), classification accuracy for the light (3-51%), moderate (12-45%), and vigorous-intensity trials (30-88%) was generally poor. All cut-points underestimated the true intensity of the walking trials, with error rates ranging from 35 to 100%, while the intensity of activity trials requiring significant upper body and/or arm movements was consistently overestimated. The Hildebrand cut-points which serve as the default option in the popular GGIR software package misclassified 30% of the light intensity trials as sedentary and underestimated the intensity of moderate and vigorous intensity trials 75% of the time.

Conclusion

Published ActiGraph cut-points for the wrist, developed specifically for school-aged youth, do not provide acceptable classification accuracy for estimating daily time spent in light, moderate, and vigorous intensity physical activity. The development and deployment of more robust accelerometer data reduction methods such as functional data analysis and machine learning approaches continues to be a research priority.

Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach

It remains unclear whether the time-use composition of 24-h movement behaviours (sleep, sedentary time (ST), physical activity (PA)) and recreational screen use are independently associated with psychosocial health. This study examined the cross-sectional and longitudinal associations between 24-h movement behaviour composition, recreational screen use and psychosocial health outcomes in children. Measures completed at baseline (n = 127; 11.7 years) and follow-up (n = 88; 12.8 years) included accelerometer-based 24-h movement behaviours, self-reported recreational screen use and psychosocial health (Strengths and Difficulties Questionnaire, Kessler’s Psychological Distress Scale). Linear mixed models were used to examine the cross-sectional and longitudinal associations between the 24-h movement behaviour composition and recreational screen use levels with psychosocial health outcomes. Overall, the movement behaviour composition (p < 0.05) and recreational screen use levels (p < 0.01) were both cross-sectionally but not longitudinally associated with psychosocial health outcomes. Relative to other behaviours, sleep was negatively associated, while light-intensity PA was positively associated with internalising problems and total difficulties scores. ST was positively associated with internalising problems. High levels of recreational screen use (>2 h/day) were associated with greater externalising problems, total difficulties scores and psychological distress. These findings reinforce the importance of achieving a balance between different types of movement behaviours over a 24-h period for psychosocial health.

Validity of GENEActiv Accelerometer Wear and Nonwear Time for Use in Infants

BACKGROUND

Tummy time is recommended by the World Health Organization as part of its global movement guidelines for infant physical activity. To enable objective measurement of tummy time, accelerometer wear and nonwear time requires validation. The purpose of this study was to validate GENEActiv wear and nonwear time for use in infants.

METHODS

The analysis was conducted on accelerometer data from 32 healthy infants (4-25 wk) wearing a GENEActiv (right hip) while completing a positioning protocol (3 min each position). Direct observation (video) was compared with the accelerometer data. The accelerometer data were analyzed by receiver operating characteristic curves to identify optimal cut points for second-by-second wear and nonwear time. Cut points (accelerometer data) were tested against direct observation to determine performance. Statistical analysis was conducted using leave-one-out validation and Bland-Altman plots.

RESULTS

Mean temperature (0.941) and z-axis (0.889) had the greatest area under the receiver operating characteristic curve. Cut points were 25.6°C (temperature) and -0.812g (z-axis) and had high sensitivity (0.84, 95% confidence interval, 0.838-0.842) and specificity (0.948, 95% confidence interval, 0.944-0.948).

CONCLUSIONS

Analyzing GENEActiv data using temperature (>25.6°C) and z-axis (greater than -0.812g) cut points can be used to determine wear time among infants for the purpose of measuring tummy time.

Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents

Background: The objective and accurate assessment of children's sedentary and physical behavior is important for investigating their relation to health. The purpose of this study is to validate a simple and robust method for the identification of sitting, standing, walking, running and biking performed by preschool children, children and adolescents in the age from 3 to 16 years from a single thigh-worn accelerometer. Method: A total of 96 children were included in the study and all subjects followed a structured activity protocol performed in the subject's normal kindergarten or school environment. Thigh acceleration was measured using the Axivity AX3 (Axivity, Newcastle, UK) device. Method development and accuracy was evaluated by equally dividing the subjects into a development and test group. Results: The sensitivity and specificity for identifying sitting and standing was above 99.3% and for walking and running above 82.6% for all age groups. The sensitivity and specificity for identifying biking was above 85.8% for children and adolescents and above 64.8% for the preschool group using running bikes. Conclusion: The accurate assessment of sitting, standing, walking, running and biking from thigh acceleration and with children in the age range of 3 to 16 is valid, although not with preschool children using running bikes.

Assessing Physical Activity Using Accelerometers in Youth with Duchenne Muscular Dystrophy

BACKGROUND

Physical activity, assessed by accelerometers, has been proposed as a quantitative outcome measure for patients with DMD, but research is limitedObjective:To assess the total amount and patterns of physical activity in patients with DMD using accelerometers.

METHODS

Physical activity was assessed in patients with DMD (n = 49, 13.6±4.0-year-old) and age- and sex-matched healthy controls (n = 15, 14.0±2.3-year-old) using wrist- and ankle-worn accelerometers. To assess the amount of activity, accelerometer recordings were converted into acceleration estimates (counts/min). Patterns of activity were assessed as the time that participants spent in sedentary, low-intensity, and moderate-to-vigorous physical activity categories. The sedentary category was divided into three (sedentary -1, -2, and -3) and the low-intensity into two (low-intensity-1, and -2) subcategories.

RESULTS

Physical activity across intensity categories differed between study groups (p < 0.001). Patients with DMD spent on average 98.8% of their daytime in the sedentary and low-intensity categories. Compared to non-ambulatory, ambulatory patients spent more time in sedentary-3 and low-intensity-2 subcategories (p < 0.001). Amount of activity was lower in all patients than controls (p < 0.05) and in non-ambulatory than ambulatory patients and controls (p < 0.001), but similar between ambulatory patients and controls. Activity measures in patients were significantly affected by age and ambulation status (p < 0.05) but not corticosteroid use.

CONCLUSION

Patients with DMD spent most of their daytime in sedentary and low-intensity activities. Dividing these intensities into three and two subcategories, respectively, allows better characterization of activity patterns in DMD. Ambulation status and age but not corticosteroid use affected activity measures in patients with DMD.

Baseline preoperative physical activity for surgical patients varies from healthy population controls

BACKGROUND

Preoperative physical activity (PA) is an important reference point to evaluate recovery, yet is not attainable for emergent surgical admissions. We investigated the validity of PA of healthy children recruited from within the same community as surgical patients and a nationally representative sample as alternative baseline PA for pediatric surgical patients.

METHODS

Patients undergoing an elective operation were matched to community-recruited healthy controls (CRHC) on sex, age, and weight, and their PA was assessed using an Actigraph accelerometer. National Health and Nutrition Examination Survey (NHANES) Actigraph PA data were used as a nationally representative match for baseline PA. Surgical patients wore the accelerometer for 2 days preoperatively, CRHC for 2 days, and NHANES participants for 7 days. PA was categorized as light (LPA) or moderate vigorous (MVPA). Means were compared between the 3 groups.

RESULTS

Thirty patients were matched with 80 CRHC and 3147 NHANES participants. LPA was similar between surgical patients and CRHC. However, CRHC averaged 19 min/day more MVPA than surgery patients (p = 0.04), and both groups averaged 58 min and 67 min/day higher MVPA than the matched NHANES sample, respectively (p < 0.01).

CONCLUSIONS

CRHC LPA was similar to preoperative LPA in surgical patients and may be an alternative.

LEVEL OF EVIDENCE

Level II.

Development of raw acceleration cut-points for wrist and hip accelerometers to assess sedentary behaviour and physical activity in 5-7-year-old children

This study validated sedentary behaviour (SB), moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) accelerometer cut-points in 5-7-year-old children. Participants (n = 49, 55% girls) wore an ActiGraph GT9X accelerometer, recording data at 100 Hz downloaded in 1 s epochs, on both wrists and the right hip during a standardised protocol and recess. Cut-points were generated using ROC analysis with direct observation as a criterion. Subsequently, cut-points were optimised using Confidence intervals equivalency analysis and then cross-validated in a cross-validation group. SB cut-points were 36 mg (Sensitivity (Sn) = 79.8%, Specificity (Sp) = 56.8%) for non-dominant wrist, 39 mg (Sn = 75.4%, Sp = 70.2%) for dominant wrist and 20 mg (Sn = 78%, Sp = 50.1%) for hip. MVPA cut-points were 189 mg (Sn = 82.6%, Sp = 78%) for non-dominant wrist, 181 mg (Sn = 79.1%, Sp = 76%) for dominant wrist and 95 mg (Sn = 79.3%, Sp = 75.6%) for hip. VPA cut-points were 536 mg (Sn = 75.1%, Sp = 68.7%) for non-dominant wrist, 534 mg (Sn = 67.6%, Sp = 95.6%) for dominant wrist and 325 mg (Sn = 78.2%, Sp = 96.1%) for hip. All placements demonstrated adequate levels of accuracy for SB and PA assessment.

Segmented sedentary time and physical activity patterns throughout the week from wrist-worn ActiGraph GT3X+ accelerometers among children 7-12 years old

BACKGROUND

This study examined the volume and patterns of physical activity (PA) and sedentary time (ST) across different segments of the week among boys and girls.

METHODS

A total of 188 children aged 7-12 years wore a wrist-mounted ActiGraph GT3X+ accelerometer for 7 days. Time spent in PA and ST was calculated using ActiLife software. The mean number of minutes of light PA, moderate PA, vigorous PA, moderate-to-vigorous PA (MVPA), and ST were calculated per weekday (before school, during school, and after school) and per weekend day (morning and afternoon-evening).

RESULTS

After school represented the greatest accumulation of ST compared with before school and during school segments. Boys engaged in 225.4 min/day of ST (95% confidence interval (CI): 216-235), and girls engaged in 222.2 min/day of ST (95%CI: 213-231). During school, boys engaged in significantly more MVPA than girls (46.1 min/day (95%CI: 44-48) vs. 40.7 min/day (95%CI: 39-43)). Across the whole weekday, boys participated in significantly more MVPA than girls (103.9 min/day (95%CI: 99-109) vs. 95.7 min/day (95%CI: 90-101)). The weekend afternoon-evening segment represented the larger accumulation of ST, where boys were significantly more sedentary than girls (367.5 min/day (95%CI: 353-382) vs. 339.8 min/day (95%CI: 325-355), respectively).

CONCLUSION

Our findings suggest that children are highly sedentary and spend little of their time in school in MVPA, especially girls. Routine breaks in school elicit increases in light PA and MVPA. Future work should consider the use of more active breaks within school time to encourage PA and reduce ST.

Sitting and Screen Time Outside School Hours: Correlates in 6- to 8-Year-Old Children

BACKGROUND

Although there is increasing evidence regarding children's screen time, little is known about children's sitting. This study aimed to determine the correlates of screen time and sitting in 6- to 8-year-old children.

METHODS

In 2011-2012, parents in the Healthy Active Preschool and Primary Years (HAPPY) study (n = 498) reported their child's week/weekend day recreational screen time and potential correlates. ActivPALs™ measured children's nonschool sitting. In model 1, linear regression analyses were performed, stratified by sex and week/weekend day and controlling for age, clustered recruitment, and activPAL™ wear time (for sitting analyses). Correlates significantly associated with screen time or sitting (P < .05) were included in model 2.

RESULTS

Children (age 7.6 y) spent 99.6 and 119.3 minutes per day on week and weekend days engaging in screen time and sat for 119.3 and 374.6 minutes per day on week and weekend days, respectively. There were no common correlates for the 2 behaviors. Correlates largely differed by sex and week/weekend day. Modifiable correlates of screen time included television in the child's bedroom and parental logistic support for, encouragement of, and coparticipation in screen time. Modifiable correlates of sitting included encouragement of and coparticipation in physical activity and provision of toys/equipment for physical activity.

CONCLUSIONS

Interventions may benefit from including a range of strategies to ensure that all identified correlates are targeted.

Concurrent validity of the ActiGraph GT3X+ and activPAL for assessing sedentary behaviour in 2-3-year-old children under free-living conditions

OBJECTIVES

ActiGraph accelerometer cut-points are commonly used to classify sedentary behaviour (SB) in young children. However, they vary from 5counts/5s to 301counts/15s, resulting in different estimates and inconsistent findings. The aim was to examine the concurrent validity of ActiGraph GT3X+cut-points against the activPAL for measuring SB in 2-3-year-olds during free-living conditions.

DESIGN

Observational validation-study.

METHODS

Sixty children were fitted with the activPAL and ActiGraph simultaneously for at least 2h. Nine ActiGraph cut-points ranging from 60 to 1488 counts per minute were used to derive SB. Bland & Altman plots and equivalent tests were performed to assess agreement between methods.

RESULTS

Estimates of SB according to the different ActiGraph cut-points were not within the activPAL ±10% equivalent interval (-4.05; 4.05%). The ActiGraph cut-points that showed the lower bias were 48counts/15s (equivalence lower limit: p= 0.597; equivalence upper limit: p<0.001; bias: -4.46%; limits of agreement [LoA]: -21.07 to 30.00%) and 5counts/5s (equivalence lower limit: p<0.001; equivalence upper limit: p= 0.737; bias: -5.11%; LoA: 30.43 to 20.20%). For the 25counts/15s, 37counts/15s and 48counts/15s ActiGraph cut-points, the upper limits were within the equivalent interval (p<0.001) but not the lower limits (p>0.05). When using the 5counts/5s and 181counts/15s ActiGraph cut-points, lower limits were within the equivalent interval (p<0.001) but not the upper limits (p>0.05). Confidence intervals of the remaining ActiGraph cut-points lie outside the equivalent interval.

CONCLUSIONS

Although none of the ActiGraph cut-points provided estimates of SB that were equivalent to activPAL; estimates from 48counts/15s and 5counts/5s displayed the smallest mean bias (˜5%).

Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities

Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler’s unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants’ performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was “carried” by an adult (median = 144 counts/5 s; p < 0.01). While standing, sitting, and riding in a stroller, very low hip vertical axis counts were registered (median ≤ 5 counts/5 s). Although wrist vertical axis and vector magnitude counts for “carried” were not higher than those for walking, they were higher than the cut-points for sedentary behaviors. Using various accelerometry signal features, machine learning techniques showed 89% accuracy to differentiate the “carried” behavior from ambulatory movements such as running, walking, crawling, and climbing. In conclusion, hip vertical axis counts alone may be unable to capture walking as physical activity and “carried” as sedentary behavior among toddlers. Machine learning techniques that utilize additional accelerometry signal features could help to recognize behavior types, especially to differentiate being “carried” from ambulatory movements.

Validity of energy expenditure estimation methods during 10 days of military training

Wearable physical activity (PA) monitors have improved the ability to estimate free-living total energy expenditure (TEE) but their application during arduous military training alongside more well-established research methods has not been widely documented. This study aimed to assess the validity of two wrist-worn activity monitors and a PA log against doubly labeled water (DLW) during British Army Officer Cadet (OC) training. For 10 days of training, twenty (10 male and 10 female) OCs (mean ± SD: age 23 ± 2 years, height 1.74 ± 0.09 m, body mass 77.0 ± 9.3 kg) wore one research-grade accelerometer (GENEActiv, Cambridge, UK) on the dominant wrist, wore one commercially available monitor (Fitbit SURGE, USA) on the non-dominant wrist, and completed a self-report PA log. Immediately prior to this 10-day period, participants consumed a bolus of DLW and provided daily urine samples, which were analyzed by mass spectrometry to determine TEE. Bivariate correlations and limits of agreement (LoA) were employed to compare TEE from each estimation method to DLW. Average daily TEE from DLW was 4112 ± 652 kcal·day^-1 against which the GENEActiv showed near identical average TEE (mean bias ± LoA: -15 ± 851 kcal^. day^-1 ) while Fitbit tended to underestimate (-656 ± 683 kcal·day^-1 ) and the PA log substantially overestimate (+1946 ± 1637 kcal·day^-1 ). Wearable physical activity monitors provide a cheaper and more practical method for estimating free-living TEE than DLW in military settings. The GENEActiv accelerometer demonstrated good validity for assessing daily TEE and would appear suitable for use in large-scale, longitudinal military studies.

Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods

PURPOSE

This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data.

METHODS

Thirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activPAL for 7 d. A random forest classifier, trained on the activPAL data, was used to detect sitting, standing, and sit-stand transitions in 5-s windows in the hip-worn accelerometer. The classifier estimates were compared with the standard accelerometer cut point, and significant differences across different bout lengths were investigated using mixed-effect models.

RESULTS

Overall, the algorithm predicted the postures with moderate accuracy (stepping, 77%; standing, 63%; sitting, 67%; sit-to-stand, 52%; and stand-to-sit, 51%). Daily level analyses indicated that errors in transition estimates were only occurring during sitting bouts of 2 min or less. The standard cut point was significantly different from the activPAL across all bout lengths, overestimating short bouts and underestimating long bouts.

CONCLUSIONS

This is among the first algorithms for sitting and standing for hip-worn accelerometer data to be trained from entirely free-living activPAL data. The new algorithm detected prolonged sitting, which has been shown to be the most detrimental to health. Further validation and training in larger cohorts is warranted.

Wrist Acceleration Cut Points for Moderate-to-Vigorous Physical Activity in Youth

PURPOSE

This study aimed to examine the validity of wrist acceleration cut points for classifying moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity.

METHODS

Fifty-seven children (5-12 yr) completed 15 semistructured activities. Three sets of wrist cut points (>192 mg, >250 mg, and >314 mg), previously developed using Euclidian norm minus one (ENMO192+), GENEActiv software (GENEA250+), and band-pass filter followed by Euclidian norm (BFEN314+), were evaluated against indirect calorimetry. Analyses included classification accuracy, equivalence testing, and Bland-Altman procedures.

RESULTS

All cut points classified MPA, VPA, and MVPA with substantial accuracy (ENMO192+: κ = 0.72 [95% confidence interval = 0.72-0.73], MVPA: area under the receiver operating characteristic curve (ROC-AUC) = 0.85 [0.85-0.86]; GENEA250+: κ = 0.75 [0.74-0.76], MVPA: ROC-AUC = 0.85 [0.85-0.86]; BFEN314+: κ = 0.73 [0.72-0.74], MVPA: ROC-AUC = 0.86 [0.86-0.87]). BFEN314+ misclassified 19.7% non-MVPA epochs as MPA, whereas ENMO192+ and GENEA250+ misclassified 32.6% and 26.5% of MPA epochs as non-MVPA, respectively. Group estimates of MPA time were equivalent (P < 0.01) to indirect calorimetry for the BFEN314+ MPA cut point (mean bias = -1.5%, limits of agreement [LoA] = -57.5% to 60.6%), whereas estimates of MVPA time were equivalent (P < 0.01) to indirect calorimetry for the ENMO192+ (mean bias = -1.1%, LoA = -53.7% to 55.9%) and GENEA250+ (mean bias = 2.2%, LoA = -56.5% to 52.2%) cut points. Individual variability (LoA) was large for MPA (min: BFEN314+, -60.6% to 57.5%; max: GENEA250+, -42.0% to 104.1%), VPA (min: BFEN314+, -238.9% to 54.6%; max: ENMO192+, -244.5% to 127.4%), and MVPA (min: ENMO192+, -53.7% to 55.0%; max: BFEN314+, -83.9% to 25.3%).

CONCLUSION

Wrist acceleration cut points misclassified a considerable proportion of non-MVPA and MVPA. Group-level estimates of MVPA were acceptable; however, error for individual-level prediction was larger.

Using accelerometers to characterize recovery after surgery in children

BACKGROUND

Assessment of recovery after surgery in children remains highly subjective. However, advances in wearable technology present an opportunity for clinicians to have an objective assessment of postoperative recovery. The aims of this pilot study are to: (1) evaluate acceptability of accelerometer use in pediatric surgical patients, (2) use accelerometer data to characterize the recovery trajectory of physical activity, and (3) determine if postoperative adverse events are associated with a decrease in physical activity.

STUDY DESIGN

Children aged 3-18-years-old undergoing elective inpatient and outpatient surgical procedures were invited to participate. Physical activity was measured using an Actigraph GT3X wristworn accelerometer for ≥2days preoperatively and 5-14days postoperatively. Time spent performing light (LPA) and moderate-to-vigorous physical activity (MVPA) was expressed in minutes/day. Physical activity for each postoperative day was calculated as a percentage of preoperative activity, and recovery trajectories were produced. Adverse events were reported and mapped against recovery trajectories.

RESULTS

Of 60 patients enrolled, 25 (10 inpatients, 15 outpatients) completed the study procedures and were included in the analysis. For outpatient procedures, LPA recovered to preoperative level on postoperative day (POD) 7 and MVPA peaked at 90% on POD 8. For inpatient procedures, LPA peaked at 70% on POD 11, and MVPA peaked at 53% on POD 10. Adverse events in 2 patients were associated with a decline in activity.

CONCLUSIONS

This study demonstrates that objective monitoring of postoperative physical activity using accelerometers is feasible in the pediatric surgical population. Recovery trajectories for inpatient and outpatient procedures differ. Accelerometer technology presents clinicians with a new potential tool for assessing and managing surgical recovery, and for determining if children are not recovering as expected.

TYPE OF STUDY

Diagnostic Study.

LEVEL OF EVIDENCE

III.

Wear compliance, sedentary behaviour and activity in free-living children from hip-and wrist-mounted ActiGraph GT3X+ accelerometers

The GT3X+ worn at the wrist promotes greater compliance than at the hip. Minutes in SB and PA calculated from raw accelerations at the hip and wrist provide contrasting estimates and cannot be directly compared. Wear-time for the wrist (15.6 to 17.4 h.d^-1) was greater than the hip (15.2 to 16.8 h.d^-1) across several wear-time criteria (all P < 0.05). Moderate-strong associations were found between time spent in SB (r = 0.39), LPA (r = 0.33), MPA (r = 0.99), VPA (r = 0.82) and MVPA (r = 0.81) between the two device placements (All P < 0.001). The wrist device detected more minutes in LPA, MPA, VPA and MVPA whereas the hip detected more SB (all P = 0.001). Estimates of time in SB and all activity outcomes from the wrist and hip lacked equivalence. One hundred and eighty-eight 9-12-year-old children wore a wrist- and hip-mounted accelerometer for 7 days. Data were available for 160 (hip) and 161 (wrist) participants. Time spent in SB and PA was calculated using GGIR. This study examined the compliance of children wearing wrist- and hip-mounted ActiGraph GT3X+ accelerometers and compared estimates of sedentary behaviour (SB) and physical activity (PA) between devices.