Validation of thigh-based accelerometer estimates of postural allocation in 5-12 year-olds

Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children

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.

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.

Adaptation of a Danish online version of the Oxford Physical Activity Questionnaire (OPAQ) for secondary school students—a pilot study

Objective

To adapt and partly validate a Danish online version of the patient-reported outcome measure (PROM) Oxford Physical Activity Questionnaire (“OPAQ”) and evaluate mobile phones and tablets as data capturing tool to identify potential problems and deficiencies in the PROM prior to implementation in the full study.

Methods

The OPAQ was translated into Danish by a formalised forward-backward translation procedure. Face validity was examined by interviewing 12 school students aged 10–15, recruited from two Danish public schools. After modifications, the online version of the Danish OPAQ was pilot tested in a convenience sample of seven school students for 1 week. Simultaneous objective accelerometer data were captured during the registration period.

Results

No major challenges were identified when translating OPAQ. Based on the interviews, the Danish version of OPAQ was perceived to be easy to understand in general, and the questions were relevant for tracking activities during the week. Five of the 12 participants had difficulties with understanding the introductory question: “what is your cultural background” in the original OPAQ. The interviews revealed that the participants recalling 7 days forgot to record some of the physical activity they had done during the week, indicating issues with the weekly recall method.

After transforming to the online version, this was reported to be easy and quick to fill in (taking 1–3 min per day), and participants reported the daily design was helpful to remember activities. There was good correspondence between the online version and objective actigraphs with a tendency to underreport. Six participants reported 10–60 min less moderate to vigorous physical activity compared to the actigraphs, while one participant reported 3 min more.

Conclusion

Participants found the online OPAQ quick and easy to complete during a 1-week period. Completing daily rather than weekly may help limit issues with recall. Overall, there was good agreement between the objective actigraphs and the OPAQ, though the OPAQ tended to slightly underreport moderate to vigorous physical activity.

The Danish online version of OPAQ may be useful for capturing school students’ physical activity when objective measures are not feasible.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40814-022-01108-x.

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.

Comparability of ActivPAL-Based Estimates of Meeting Physical Activity Guidelines for Preschool Children

The activPAL (PAL Technologies, Glasgow, UK) has been increasingly used on children to assess sedentary time and physical activity (PA). However, there is no consensus on how it can estimate PA at different intensities. This study compared three commonly used, activPAL-based classifications of moderate-to-vigorous physical activity (MVPA) (daily steps, acceleration counts, and step rate) in determining compliance with the World Health Organization (WHO)’s PA guidelines for preschool children on a daily basis. One hundred and fourteen preschool children aged 3–6 years wore an activPAL^TM for 24 h over 7 consecutive days and provided valid data for a total of 548 days. MVPA was calculated based on published cut-points of counts (MVPA-counts) and step rate (MVPA-step rate). Compliance with standard PA guidelines (≥180 min/day of PA including ≥60 min/day of MVPA) was determined based on three criteria: ≥11,500 steps/day, a threshold of 1418 acceleration counts/15 s, and 25 steps/15 s for MVPA. Applying cut-points of daily steps and acceleration counts provided the same estimates of compliance with the WHO PA guidelines (20%), while the estimated compliance based on the step rate was lower (7.7%). There was a moderate agreement between the daily steps- (or counts-) derived and step rate-derived compliances (κ = 0.41; 95% confidence interval (CI): 0.31, 0.51). The amount of MVPA derived from counts (1.95 ± 0.72 h/day) was significantly higher than that from step rates (0.47 ± 0.31 h/day). The activPAL may be useful for surveillance studies to estimate total PA in preschool children. Further development of the activPAL algorithms based on either counts or step rate is warranted before it can be used to accurately estimate MVPA in this age group.

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%).

Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children

INTRODUCTION

This study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children.

METHODS

Fifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (≤25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC).

RESULTS

The most accurate ActiGraph wrist cut points (Kim; vector magnitude, ≤3958 counts per 60 s; vertical axis, ≤1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P < 0.01; -2.5%, -27.9% to 22.9%). The mean bias of SB time estimates from Kim were smaller than ActiGraph hip (5-8 yr: 15.8%, -5.7% to 37.2%; 9-12 yr: 17.8%, -3.9% to 39.5%) and similar to or smaller than activPAL3 (5-8 yr: 12.6%, -39.8% to 14.7%; 9-12 yr: -1.4%, -13.9% to 11.0%), although classification accuracy was similar to ActiGraph hip (ROC-AUC = 0.85) but lower than activPAL3 (ROC-AUC = 0.92-0.97). Mean bias (5-8 yr: 6.5%, -16.1% to 29.1%; 9-12 yr: 10.5%, -13.6% to 34.6%) for the most accurate GENEActiv wrist cut point (Schaefer: ≤0.19 g) was smaller than ActiGraph hip, and activPAL3 in 5-8 yr, but larger than activPAL3 in 9-12 yr. However, SB time estimates from Schaefer were not equivalent to direct observation (equivalence P > 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3.

CONCLUSION

The most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (≤25 counts per 15 s), although activPAL3 was generally more accurate.