Physical activity collections

Physical activity, minimal sedentary behaviour, and quality sleep are important for good health at all stages of life. In Australia, national guidelines set out how much activity, sitting time, and sleep people should aim for to support their physical and mental wellbeing. Being physically active can improve mood and reduce the risk of long-term conditions such as heart disease, while too much sitting or screen time can negatively affect health. Sleep is also essential, helping with growth, learning, and overall functioning. Because health needs change with age, the guidelines differ for toddlers, children, and adults.

Physical activity, sedentary behaviour and sleep data is available in the following four IHMHS collections:

• The National Health Survey (NHS), 2022
• The National Aboriginal and Torres Strait Islander Health Survey (NATSIHS), 2022-2023
• The National Nutrition and Physical Activity Survey (NNPAS), 2023
• The National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (NATSINPAS), 2023

While each of the four health surveys in the IHMHS collected physical activity, sedentary behaviour and sleep data, the types of questions were tailored to each survey. This is due to survey specific priorities, consistency and timeseries with previous survey iterations and target populations.

The IHMHS obtained self-reported estimates of physical activity, sedentary behaviour and sleep through its survey questionnaires. These questions generally focused on the types and amounts of physical activity people did for leisure, exercise and while working. Specific questions also related to the amount of time people spent sitting or lying down. Survey questions were designed to assess people against national physical activity guidelines.

In addition to the variations in questions, each survey collected data from different age groups. The age groups in each survey are shown in the table below.

For physical activity collections within the IHMHS, it is necessary to define children as either pre-school aged or school aged to align with national physical activity, sedentary behaviour and sleep guidelines. In the NNPAS 2023 and NATSINPAS 2023, children are defined as:

• pre-school aged children: 2–4 years old and 5 year old children not attending primary school
• school aged children and young people: 6–17 years old and 5 year old children attending primary school.

Young persons aged 15–17 years old in the NHS 2022 and NATSIHS 2022-23 are not defined by whether they attend school or not.

In some circumstances, a person selected for a survey may have been unable to complete the survey questionnaire themselves. A suitable adult from the household assisted or answered on that person’s behalf. This is called a proxy interview.

These interviews can be conducted in three circumstances:

• For children aged 2–14 years, an appropriate adult from the household responded to the questions on the child’s behalf. The child may or may not have been present during the interview.
• For young people aged 15–17 years, a parent or guardian could provide consent for the young person to answer the questions themselves. If consent was not granted, an appropriate adult from the household responded on the child’s behalf. 
• In some cases, an adult respondent required assistance to complete the questionnaire. For example, due to language barriers or certain disabilities.

Some questions were only asked during these types of interviews if the selected person was present. This approach helped ensure the most accurate responses possible. 

The reference period for self-reported physical activity was typically based on the week prior to interview. However, the number of days respondents were asked about varied across survey collections and between non-remote and remote areas. The table below outlines the number of reported days by collection, remoteness area and age group. 

Note, there may be additional minor differences for specific topics.

The reference periods used in the NNPAS 2023 and NATSINPAS 2023 (non-remote) self-reported physical activity, sedentary behaviour and sleep were determined based on user requirements, the results of questionnaire testing, and a desire to minimise respondent burden and maximise data quality. Testing indicated that a shorter time period of three days was the preferred approach for reporting on children's physical activity and sedentary behaviours in NATSINPAS. This resulted in the reference period for these components being limited to the last three days in NATSINPAS 2023 and the last seven days in NNPAS 2023. Only broad physical activity on the day before interview was collected in remote areas for NATSIHS 2022-23 and NATSINPAS 2023.

The IHMHS obtained directly measured physical activity estimates from wrist-worn devices called accelerometers. Accelerometers are a common type of sensor used to study human movement. They are wearable devices that measure linear acceleration – the change in a person’s speed (velocity) per unit time. This data is available in the:

• National Nutrition and Physical Activity Survey, 2023
• National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey, 2023.

Directly measured physical activity, inactivity and sleep data comes from respondents aged 5 years and over who volunteered to wear an accelerometer for up to 7 days as part of the study.

As the accelerometer is worn on the wrist and measures acceleration through movement of the wrist, there are limitations to its ability to measure sedentary behaviour (that is, sitting and lying down) in the same way that the self-reported questions do. Estimates from accelerometers measure the absence of movement, or minimal movement, referred to as ‘Inactivity’. These may include sedentary behaviour, sleep, as well as standing still for periods of time. 

For more information, see the Sedentary behaviour and inactivity section.

Participation in the accelerometer component of the survey was voluntary, and participation rates varied by age group. Differences in opt-in rates, and valid wear time contribute to variation in sample sizes across age groups. Further information on participation patterns and wear time requirements is available in the NNPAS 2023 and NATSINPAS 2023 methodology.

Directly measured physical activity estimates were derived using age specific acceleration thresholds to account for differences in physiology and movement patterns between children aged 5–17 years and adults aged 18 years and over. Different cut-points (thresholds) were applied for children aged 5–17 years and adults aged 18 years and over when classifying inactivity, light, moderate and vigorous physical activity. These thresholds were based on research by Hildebrand et al.[6] showing that equivalent levels of energy expenditure correspond to different levels of wrist acceleration by age.

The Hildebrand acceleration thresholds were developed in studies that assessed physical activity in children aged 7–11 years and adults aged 18–65 years, leaving the 12–17 year old and 66 years and over age groups without calibrated cut-points. Following consultation with accelerometer researchers and prospective data users, it was recommended that persons aged 12–17 years be treated using the children’s cut-points, and that persons aged 66 years and over use the adult thresholds. As a result, estimates produced for adults may not be comparable to estimates produced for children. Any observed differences across these two age groupings may be a result of these different thresholds rather than true behavioural differences. See the Physical activity section for more details.

The device was generally worn for a 7 day wear period commencing at the end of the interview. 

• For NNPAS 2023 and the non-remote and remote non-community sample for NATSINPAS 2023, respondents were asked to wear the accelerometer for 7 days, then post back using a pre-paid envelope. 
• For the remote discrete Indigenous community-based sample in NATSINPAS 2023, respondents were asked to wear the accelerometer for 4 days, and the accelerometer was collected by an ABS Engagement Manager who posted them back to the ABS. 

ABS sought advice from accelerometry experts on the minimum amount of data that could be collected to produce reliable daily estimates. The shorter wear time for the remote community sample enabled reliable estimates to be produced while maximising the likelihood of a successful return of the accelerometers which were able to be collected by ABS Engagement Managers while they were still in community. 

Microdata from the survey includes a complete full 7 day (168 hour) wear period for all participating respondents, irrespective of the actual duration the device was worn. Information on wear time rates and the way the ABS handled missing data is available in the relevant survey methodology. 

Certain aspects of survey administration may influence the data. To support appropriate analysis, administration flags have been included in the microdata. These flags allow users to account for collection characteristics, including:

• the start day of the wear period, which is determined by the day of the week of interview
• the start time of the wear period, which is determined by the approximate time of interview.

1. van Hees, V., Migueles, J., Fang, Z., Zhao, J., Heywood, J., Mirkes, E., Sabia, S. 2025. GGIR: Raw Accelerometer Data Analysis. R package version 3.2-6. https://doi.org/10.5281/zenodo.1051064https://CRAN.R-project.org/package=GGIR
2. R Core Team. 2024. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
3. Migueles, J. H., Rowlands, A. V., Huber, F., Sabia, S., van Hees, V. T. 2019. GGIR: A research community–driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. Journal for the Measurement of Physical Behaviour, 2(3), 188–196. https://doi.org/10.1123/jmpb.2018-0063
4. van Hees, V., Fang, Z., Langford, J., Assah, F., Mohammad, A., da Silva, I., Trenell, M., White, T., Wareham, N., Brage, S. 2014. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: An evaluation on four continents. Journal of Applied Physiology, 117(7), 738–744. https://doi.org/10.1152/japplphysiol.00421.2014
5. van Hees, V., Sabia, S., Anderson, K., Denton, S., Oliver, J., Catt, M., Abell, J., Kivimäki, M., Trenell, M., Singh-Manoux, A. 2015. A novel, open access method to assess sleep duration using a wrist-worn accelerometer. PLoS ONE, 10(11). https://doi.org/10.1371/journal.pone.0142533
6. Hildebrand, M., Hansen, B., van Hees, V., Ekelund, U. 2016. Evaluation of raw acceleration sedentary thresholds in children and adults. Scandinavian Journal of Medicine & Science in Sports, 27(12), 1814–1823. https://doi.org/10.1111/sms.12795