Physical activity measurement development

Background: The World Health Organization (WHO) has underscored the significance of monitoring behaviours in children beneath the age of five years. Considering this, global studies such as the SUNRISE initiative have been conducted. Wearable technology is capable of tracking daily step counts; however, research predominantly from high-income countries highlights the need for global reference values. Purpose: This study aimed to provide age-group reference percentile values for daily step counts in young children, and to investigate factors that contribute to variations, such as income level. Methods: The study utilized the International SUNRISE Study protocol to collect data from 2815 children across 33 countries. Quantile regression techniques were then applied to examine how daily step counts varied with age. Results: The results of the study showed that age significantly influenced step count, with varying effects observed at different percentiles. Income differences also significantly affected the daily step count, particularly at the 75th percentile, indicating a pronounced effect. Conclusions: The findings of this study emphasize the need for reference values to optimize behaviors, as socioeconomic level influences movement in children under five. These results offer insight into the relationship between step count, age, and income, which can inform public health initiatives for young children. Practical implications: The study provides a foundation for future research and policy decisions regarding general movement and serves as a reference guide for this purpose. Funding: The study was supported by the Sunrise Global project, the government of Andalucia (P20\_01181), and the European Union through the requalification of university teaching staff – Ministry of Universities (22330, 22334).

Objective: This cross-sectional study aims to assess the agreement between self-reported MVPA and VPA in leisure time, as reported in the international Health Behaviour in School-aged Children (HBSC) study, and device-based measures from accelerometers in a large national sample of Danish adolescents. Methods: A total of 2400 adolescents (54% girls) aged 11-15 years wore accelerometers (Axivity AX3) to measure MVPA and VPA. They self-reported the number of days (0-7) they engaged in at least 60 minutes of physical activity per day. Compliance with physical activity guidelines was defined as 7 days of self-reported MVPA (of at least 60 minutes per day) and at least 60 minutes of MVPA per average day according to accelerometry. Cohen’s Kappa correlations were used to analyze the association between self-report and device-based measurements for the entire sample, as well as gender and age-stratified groups for meeting MVPA guidelines and VPA in leisure time. Results: Survey data shows that 13% meet the recommendation of being physically active every day, while accelerometer data indicates that this applies to 32%. Thus, the survey method underestimates the proportion of those who adhere to the recommendations for physical activity. The study found low to fair correlations (0.16–0.30) between self-report and accelerometer MVPA across gender and age groups, with the lowest correlations among the youngest age group. The agreement between self-report and accelerometry data was good for specificity (91 %), but sensitivity was low (23 %), possibly due to poor compliance with recommendations. Comparing self-reported and accelerometer-measured days with VPA in leisure time, showed fair to moderate correlations (0.29-0.49) across gender and age groups, with the highest correlation found for 15-year-olds. Conclusions: The self-report questionnaire for MVPA and VPA demonstrates low to moderate validity compared to accelerometry, varying depending on the specific physical activity item and sex and age groups.

Background: How much time children spent in different postures and movements (PaMs, lying, sitting, standing, walking, running and stair-climbing) is important for their health and development. Thus, accurate measurements of PaMs are essential to understand the daily patterns and their impact on children’s development – information that can be obtained using wearable-based devices, such as accelerometers. While an increasing number of studies are using accelerometry among children, few studies have assessed the validity of machine/deep learning models for identifying a range of PaMs. Purpose: The purpose of this study was to evaluate the validity of different machine/deep learning models to recognise PaMs in children between 3 and 14 years old using data from thigh-mounted accelerometers in a laboratory setting. Methods: A selection of traditional machine learning models (Unbalanced and Balanced Random Forests, Support Vector Machines, K Nearest Neighbours and XGBoost) and a deep learning model (Long Short-Term Memory-Convolutional Neural Network with an increasing number of layers) were trained using human coded video from 36 children as the reference standard. Models were tested on a further unseen 12 participants. Results: Both types of models were found to perform well with the best model for each type having an accuracy ≥ 92% and macro F1 score ≥ 80%. Data processing options such as window length and normalisation method made substantial differences to model performance. Confusion matrices showed that some models performed better with common PaMs (e.g. sitting, lying) and others performed better with less commonly occurring PaMs (e.g. running, stair-climbing). Conclusions: Machine learning and deep learning models can accurately classify common postures and movements of children. Practical implications: Machine/deep learning can be used for large-scale cohort studies and surveillance to help inform targeted interventions to increase physical activity in children. Funding: The Australian Research Council Centre of Excellence for the Digital Child.

Background & Purpose: People with learning disabilities can be affected by complex health needs and their life expectancy is significantly reduced. This is considered as a priority population with levels of physical activity estimated to be less than half of those without a disability. Physical activity has a role in enhancing quality of life and better management of multiple health issues in this population especially if they are individually tailored to the service users’ abilities and care needs. Considering the complexities of communication and intellectual challenges, there is a need for a specific physical activity assessment tool in people with learning disabilities. Design/methodology: A multidisciplinary team of experts devised the Learning Disability Physical Activity Questionnaire (LDPAQ) as a tool to measure physical activity. The tool was tested first within community followed by inpatient setting for comparison and assessment of its reliability and paracticality in two different environments. Findings: An easy-read, picture-based, self-reported and concise questionnaire with options relevant to people with learning disabilities was developed. Feedback from the audit confirmed ease of use, relevance and high levels of respondent satisfaction in both community and inpatient settings. A small-scale audit of the tool also confirmed the need for promoting physical activity within this population. Conclusion: The LDPAQ is a novel questionnaire that aims to be a universally applicable tool for the assessment of physical activity status in people with learning disabilities. It is designed to be used by people with learning disabilities themselves, professionals and organisations and can provide time efficient reliable measurements that can inform and guide health care practitioners, care givers and service users . Further larger scale collaborative research is needed to explore the full potential of this tool.

Background: Recent advances in wearable sensor technology may enable robust systems for measurement of children’s postures and movement (e.g. sitting, walking, and running). Such systems can assist in understanding the impact of various posture and movement on children’s health and wellbeing. Motus, a wearable sensor-based system for surveillance of postures and movements, has shown high accuracy among adults. However, its accuracy among children is unknown. Purpose: This study aimed to evaluate the accuracy of Motus to measure common postures and movements among children between 3-14 years old in a laboratory setting. Method: Data were collected on 48 children who attended a structured laboratory-based ~1-hour data collection session. The session was video recorded and thigh acceleration was measured using a SENS accelerometer. Data from the accelerometer were processed and classified into six postures and movements using the Motus software. Human-coded video analysis provided the gold standard to calculate sensitivity, specificity, and balanced accuracy. Results: We observed high sensitivity, specificity, and balanced accuracy for classifying lying, sitting, standing, walking, and running (balanced accuracy ranging between 72.0-98.4%). The lowest balanced accuracy was observed for classifying stair climbing (65.7%). Conclusion: Motus showed high balanced accuracy for detecting lying, sitting, standing, and running among children. However, the system could be improved for classifying stair climbing and developed further to measure more child-specific postures and movements. Practical implications: We found that Motus could be a suitable tool for measuring lying, sitting, standing, walking, and running among children. Future work is planned to test the feasibility of using Motus to measure postures and movements among children in free-living settings. Funding: This study was partly funded by the Australian Research Council through the ARC Centre of Excellence for the Digital Child, grant number CE200100022 and the Curtin School of Allied Health 2022 Teaching and Research Grant.

Background: Raw accelerometer thresholds were based on absolute intensity and developed in lab-based environments. Purpose: To use Ecological Momentary Assessment (EMA) to estimate raw accelerometer thresholds using ‘naturally occurring’ activities in different domains, based on measures of perceived exertion in mid-age adults in Brisbane. Methods: Participants (N= 50, 40-65 years) wore an ActiGraph tri-axial accelerometer (GT9x Link) on the non-dominant wrist and responded to six EMA questionnaires per day for seven days. EMA questions were used to collect information about type and duration of each activity, as well as rate of perceived exertion (0-10) of the activity conducted prior to the prompt. Linear mixed models were used to estimate perceived exertion values and acceleration outputs (mg) and to estimate ranges of acceleration for three categories of perceived exertion [less than moderate (0-2); less than severe (3-5); severe or more (6-10)] Results: Median acceleration for categories of intensity based on perceived exertion were 36.0 mg for “less than moderate”, 74.5 mg for “less than severe” and 87.5 mg for “severe or more”. The proposed intensity thresholds for intensity based on perceived exertion were 89 mg for “less than severe” and 385 mg for “severe or more”. Conclusions: The widely adopted raw accelerometer thresholds may be too high to detect vigorous activity in a real-world setting. Thresholds estimated in this study, based on perceived exertion can be used in future studies as they have ecological validity. Funding: Ruth Brady is supported by a University of Queensland and University of Exeter (QUEX) Institute Studentship. Gregore Mielke is supported by a National Health and Medical Research Council Investigator Grant. Keywords: raw accelerometer thresholds, Moderate physical activity, vigorous physical activity,