Australian Health Survey: Physical Activity methodology

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Reference period
2011-12 financial year
Released
19/07/2013
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Explanatory notes

Introduction

1 This publication presents a selection of results from the 2011-12 National Nutrition and Physical Activity Survey (NNPAS), with the focus on physical activity, sedentary behaviour and pedometer steps.

2 The 2011-12 NNPAS was conducted throughout Australia from May 2011 to June 2012. NNPAS was collected as one of a suite of surveys conducted from 2011-2013, called the Australian Health Survey (AHS). The National Health Survey (NHS), also a part of the AHS, collected similar data on adult physical activity, of which level of exercise was reported in the Australian Health Survey: First Results, 2011-12 (cat. no. 4364.0.55.001) and was also used in cross-tabulations for the Australian Health Survey: Health Service Usage and Health Related Actions, 2011-12 (cat. no. 4364.0.55.002).

3 The 2011-12 NNPAS physical activity publication contains information about physical activity (including pedometer steps) and sedentary behaviour (in particular screen-based activity) and comparisons with:

  • health-related aspects of people's lifestyles, such as smoking, Body Mass Index, blood pressure and fruit and vegetable intake
  • demographic and socioeconomic characteristics.
     

4 The statistics presented in this publication are only a selection of the information collected in the NNPAS. Further publications from the Australian Health Survey are outlined in the Release Schedule, while the list of data items currently available from the survey are available in the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

Scope of the survey

5 The National Nutrition and Physical Activity Survey (NNPAS) contains a sample of approximately 9,500 private dwellings across Australia.

6 Urban and rural areas in all states and territories were included, while Very Remote areas of Australia and discrete Aboriginal and Torres Strait Islander communities (and the remainder of the Collection Districts in which these communities were located) were excluded. These exclusions are unlikely to affect national estimates, and will only have a minor effect on aggregate estimates produced for individual states and territories, excepting the Northern Territory where the population living in Very Remote areas accounts for around 23% of persons.

7 Non-private dwellings such as hotels, motels, hospitals, nursing homes and short-stay caravan parks were excluded from the survey. This may affect estimates of the number of people with some chronic health conditions (for example, conditions which may require periods of hospitalisation).

8 Within each selected dwelling, one adult (aged 18 years and over) and, where possible, one child (aged 2 years and over) were randomly selected for inclusion in the survey. Sub-sampling within households enabled more information to be collected from each respondent than would have been possible had all usual residents of selected dwellings been included in the survey.

9 The following groups were excluded from the survey:

  • certain diplomatic personnel of overseas governments, customarily excluded from the Census and estimated resident population
  • persons whose usual place of residence was outside Australia
  • members of non-Australian Defence Forces (and their dependents) stationed in Australia
  • visitors to private dwellings.
     

Data collection

    10 Trained ABS interviewers conducted personal interviews with selected residents in sampled dwellings. One person aged 18 years and over in each dwelling was selected and interviewed about their own health characteristics. An adult, nominated by the household, was interviewed about one child (aged 2 years and over) in the household. Selected children aged 15-17 years may have been personally interviewed with parental consent. An adult, nominated by the household, was also asked to provide information about the household, such as the combined income of other household members. Children aged 6-14 years were encouraged to be involved in the survey, particularly for the physical activity module. For further information, see Data Collection in the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

    11 All selected persons were required to have a follow-up phone interview at least 8 days after the face to face interview to collect further nutrition data. For those who opted in, pedometer data was reported during this telephone interview.

    Survey design

    12 Dwellings were selected at random using a multistage area sample of private dwellings for NNPAS.

    The initial sample selected for the survey consisted of approximately 14,400 dwellings. This was reduced to approximately 12,400 dwellings after sample loss (for example, households selected in the survey which had no residents in scope of the survey, vacant or derelict buildings, buildings under construction). Of those remaining dwellings, 9,519 (or 77.0%) were fully or adequately responding, yielding a total sample for the survey of 12,153 persons (aged 2 years and over).

    NNPAS, approached sample, final sample and response rates

     New South WalesVictoriaQueenslandSouth AustraliaWestern AustraliaTasmaniaNorthern TerritoryAustralian Capital TerritoryAustralia
    Households approached (after sample loss)2 2271 9831 9881 5511 5451 1559111 00612 366
    Households in sample1 6661 3711 5251 2111 3341 0035928179 519
    Response rate (%)74.869.176.778.186.386.865.081.277.0
    Persons in sample2 1391 7491 9641 5261 7061 2457631 06112 153

    13 The physical measures module of the NNPAS was voluntary. In 2011-12, 83.7% of respondents aged 2 years and over had their height and weight measured. As a proportion of the Australian population, 84.9% of persons aged 2 years and over have a height and weight measurement. BMI data from the NNPAS presented in this publication relates to the measured population only. Analysis of the characteristics of people who agreed to be measured compared to those who declined across the AHS suite of surveys indicated that age and sex were factors in non-response. Females were more likely to decline, and non-response increased with age.

    14 In 2011-12, 85.7% of respondents aged 18 years and over agreed to have their blood pressure measured and had a valid blood pressure reading obtained. As a proportion of the Australian population, 86.6% of persons aged 18 years and over have a valid blood pressure measurement. Blood pressure data from the NNPAS presented in this publication relates to the measured adult population only. Analysis of the characteristics of people who agreed to be measured compared to those who declined across the AHS suite of surveys indicated that age and sex were factors in non-response. Females were more likely to decline, and non-response increased with age.

    15 Of respondents aged 5 years and over, 52.8% participated in the pedometer component, and 49.0% met the pedometer day threshold for use in the reporting of selected items (see Pedometer steps chapter). Therefore, pedometer steps data presented in this publication relates to the population that met this threshold. Generally sex did not appear as a factor in non-participation, and there was a small increase in participation by age.

    16 More information on response rates is available in the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

    17 To take account of possible seasonal effects on health and nutrition characteristics, the NNPAS sample was spread randomly across a 12-month enumeration period. Between August and September 2011, survey enumeration was suspended due to field work associated with the 2011 Census of Population and Housing.

    Weighting, benchmarking and estimation

    18 Weighting is a process of adjusting results from a sample survey to infer results for the in-scope total population. To do this, a weight is allocated to each sample unit; for example, a household or a person. The weight is a value which indicates how many population units are represented by the sample unit.

    19 The first step in calculating weights for each person was to assign an initial weight, which was equal to the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 600, then the person would have an initial weight of 600 (that is, they represent 600 others). An adjustment was then made to these initial weights to account for the time period in which a person was assigned to be enumerated.

    20 The weights are calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks', in designated categories of sex by age by area of usual residence. Weights calibrated against population benchmarks compensate for over or under-enumeration of particular categories of persons and ensure that the survey estimates conform to the independently estimated distribution of the population by age, sex and area of usual residence, rather than to the distribution within the sample itself.

    21 The NNPAS was benchmarked to the estimated resident population living in private dwellings in non-Very Remote areas of Australia at 31 October 2011. Excluded from these benchmarks were persons living in discrete Aboriginal and Torres Strait Islander communities, as well as a small number of persons living within Collection Districts that include discrete Aboriginal and Torres Strait Islander communities. The benchmarks, and hence the estimates from the survey, do not (and are not intended to) match estimates of the total Australian resident population (which include persons living in Very Remote areas or in non-private dwellings, such as hotels) obtained from other sources. For NNPAS, a seasonal adjustment was also incorporated into the person weights.

    22 Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest. Estimates of non-person counts (for example, number of organised physical activities) are obtained by multiplying the characteristic of interest with the weight of the reporting person and aggregating.

    Reliability of estimates

    23 All sample surveys are subject to sampling and non-sampling error.

    24 Sampling error is the difference between estimates, derived from a sample of persons, and the value that would have been produced if all persons in scope of the survey had been included. For more information refer to the Technical note. Indications of the level of sampling error are given by the Relative Standard Error (RSE) and 95% Margin of Error (MOE).

    25 In this publication, estimates with an RSE of 25% to 50% are preceded by an asterisk (e.g. *3.4) to indicate that the estimate has a high level of sampling error relative to the size of the estimate, and should be used with caution. Estimates with an RSE over 50% are indicated by a double asterisk (e.g. **0.6) and are generally considered too unreliable for most purposes. These estimates can be used to aggregate with other estimates to reduce the overall sampling error.

    26 The MOEs are provided for all proportion and average estimates to assist users in assessing the reliability of these types of estimates. Users may find this measure is more convenient to use, rather than the RSE, in particular for small and large proportion or average estimates. The estimate combined with the MOE defines a range which is expected to include the true population value with a given level of confidence. This is known as the confidence interval. This range should be considered by users to inform decisions based on the estimate.

    27 Non-sampling error may occur in any data collection, whether it is based on a sample or a full count such as a census. Non-sampling errors occur when survey processes work less effectively than intended. Sources of non-sampling error include non-response, errors in reporting by respondents or in recording of answers by interviewers, and occasional errors in coding and processing data.

    28 Non-response occurs when people cannot or will not cooperate, or cannot be contacted. Non-response can affect the reliability of results and can introduce a bias. The magnitude of any bias depends on the rate of non-response and the extent of the difference between the characteristics of those people who responded to the survey and those who did not.

    29 The following methods were adopted to reduce the level and impact of non-response:

    • face-to-face interviews with respondents
    • the use of interviewers, where possible, who could speak languages other than English
    • follow-up of respondents if there was initially no response
    • weighting to population benchmarks to reduce non-response bias.
       

    30 By careful design and testing of the questionnaire, training of interviewers, and extensive editing and quality control procedures at all stages of data collection and processing, other non-sampling error has been minimised. However, the information recorded in the survey is essentially 'as reported' by respondents, and hence may differ from information available from other sources, or collected using different methodology. For example:

    • information on intensity level of physical activities was self-reported and relies on recall. An adult respondent's level of personal fitness, their age and sex, and how they measure the level of intensity for a particular activity or understand the definitional descriptions in the questions may influence their perception of the intensity level of particular physical activities, and thus which activities they allocated to the categories of moderate or vigorous. Studies which use objective methods such as accelerometers, which do not rely on respondent perception, may therefore differ in results to those collected in self-reported surveys.
       

    Classifications

    31 The classifications used to describe adult and child physical activity in this publication were developed based on those used in other ABS surveys:

    32 Country of birth was classified to the Standard Australian Classification of Countries (SACC), Second Edition, (cat. no. 1269.0).

    33 Main occupation was classified to Australian and New Zealand Standard Classification of Occupations (ANZSCO), First Release, Revision 1, 2009 (cat. no. 1221.0).

    34 Descriptions for data items such as Body Mass Index and Blood pressure are included in the Glossary to this publication.

    Results of the survey

    35 The NNPAS has not been collected in its current form before. However, some other ABS surveys have reported results for similar physical activity or sedentary behaviour data. These include:

    • Summary results of previous National Health Surveys were published separately in National Health Survey: Summary of Results, Australia, 1989-90, 1995, 2001, 2004-05 and 2007-08 (cat. no. 4364.0). Data presented, similar to NNPAS, include levels of adult physical activity.
    • General population summary results from the current cycle (2011-12), known as the Australian Health Survey are published separately under catalogue numbers identified by 4364.0.55.XXX. Data presented, similar to NNPAS, include levels of adult physical activity, published in Australian Health Survey: First Results (cat. no. 4364.0.55.001) and was also used in cross-tabulations for the Australian Health Survey: Health Service Usage and Health Related Actions, 2011-12 (cat. no. 4364.0.55.002) datacubes. Data from these two publications were drawn from the NHS sample.
    • Summary results from the Survey of Children's Participation in Cultural and Leisure Activities were published separately in Children's Participation in Cultural and Leisure Activities, Australia, Apr 2000, 2003, 2006, 2009, 2012 (cat. no. 4901.0). Data presented, similar to NNPAS, include child participation in organised sports, watching tv and internet use.
    • Summary results from the Participation in Sport and Physical Recreation Survey were published separately in Participation in Sport and Physical Recreation, Australia, 1995-1996, 1996-1997, 1997-1998, 1998-1999, 1999-2000, 2002, 2005-06, 2009-10, 2011-12 (cat. no. 4177.0). Note: pre-2005-06 the publication was known as Participation in Sport and Physical Activities, Australia. Data presented, similar to NNPAS, include adult participation in sport and recreation, with some children's data included in 1995-96 and 1996-97.
       

    In addition, the General Social Survey (cat. no. 4159.0) also collects information on adult participation in sports.

    36 While the above surveys collect similar data to NNPAS, comparisons are generally not recommended due to varying methodologies and different question reference periods and terminology.

    37 Further information about the comparability of data between surveys is in the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

    Confidentiality

    38 The Census and Statistics Act, 1905 provides the authority for the ABS to collect statistical information, and requires that statistical output shall not be published or disseminated in a manner that is likely to enable the identification of a particular person or organisation. This requirement means that the ABS must take care and make assurances that any statistical information about individual respondents cannot be derived from published data.

    39 Some techniques used to guard against identification or disclosure of confidential information in statistical tables are suppression of sensitive cells, random adjustments to cells with very small values, and aggregation of data. To protect confidentiality within this publication, some cell values may have been suppressed and are not available for publication but included in totals where applicable. As a result, sums of components may not add exactly to totals due to the confidentialisation of individual cells.

    Rounding

    40 Estimates presented in this publication have been rounded. As a result, sums of components may not add exactly to totals.

    41 For pedometer and other physical activity data, minutes and number of steps are reported as whole numbers. All other units in the data are reported to one decimal place.

    42 Proportions presented in this publication are based on unrounded figures. Calculations using rounded figures may differ from those published.

    Acknowledgements

    43 ABS publications draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated; without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act, 1905.

    Products and services

    44 Summary results from this survey are available in spreadsheet form from the 'Data downloads' section in this release.

    45 For users who wish to undertake more detailed analysis of the survey data, Survey Table Builder will also be made available in 2013. Survey Table Builder is an online tool for creating tables from ABS survey data, where variables can be selected for cross-tabulation. It has been developed to complement the existing suite of ABS microdata products and services including Census TableBuilder and CURFs. Further information about ABS microdata, including conditions of use, is available via the Microdata section on the ABS web site.

    46 Special tabulations are available on request. Subject to confidentiality and sampling variability constraints, tabulations can be produced from the survey incorporating data items, populations and geographic areas selected to meet individual requirements. A list of currently available data items is available from the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

    Related publications

    48 Current publications and other products released by the ABS are listed on the ABS website www.abs.gov.au. The ABS also issues a daily Release Advice on the website which details products to be released in the week ahead.

    Technical note

    Reliability of the estimates

    1 Two types of error are possible in an estimate based on a sample survey: sampling error and non-sampling error. The sampling error is a measure of the variability that occurs by chance because a sample, rather than the entire population, is surveyed. Since the estimates in this publication are based on information obtained from occupants of a sample of dwellings they are subject to sampling variability; that is they may differ from the figures that would have been produced if all dwellings had been included in the survey. One measure of the likely difference is given by the standard error (SE). There are about two chances in three that a sample estimate will differ by less than one SE from the figure that would have been obtained if all dwellings had been included, and about 19 chances in 20 that the difference will be less than two SEs.

    2 Another measure of the likely difference is the relative standard error (RSE), which is obtained by expressing the SE as a percentage of the estimate. The RSE is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer also to the size of the estimate.

    \(R S E \% =\left(\frac{S E}{e s t i m a t e}\right) \times 100\)

    3 RSEs for the published estimates and proportions are supplied in the Excel data tables, available via the Data downloads section.

    4 The smaller the estimate the higher is the RSE. Very small estimates are subject to such high SEs (relative to the size of the estimate) as to detract seriously from their value for most reasonable uses. In the tables in this publication, only estimates with RSEs less than 25% are considered sufficiently reliable for most purposes. However, estimates with larger RSEs, between 25% and less than 50% have been included and are preceded by an asterisk (e.g. *3.4) to indicate they are subject to high SEs and should be used with caution. Estimates with RSEs of 50% or more are preceded with a double asterisk (e.g. **0.6). Such estimates are considered unreliable for most purposes.

    5 The imprecision due to sampling variability, which is measured by the SE, should not be confused with inaccuracies that may occur because of imperfections in reporting by interviewers and respondents and errors made in coding and processing of data. Inaccuracies of this kind are referred to as the non-sampling error, and they may occur in any enumeration, whether it be in a full count or only a sample. In practice, the potential for non-sampling error adds to the uncertainty of the estimates caused by sampling variability. However, it is not possible to quantify the non-sampling error.

    Standard errors of proportions and percentages

    6 Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. For proportions where the denominator is an estimate of the number of persons in a group and the numerator is the number of persons in a sub-group of the denominator group, the formula to approximate the RSE is given below. The formula is only valid when x is a subset of y.

    \(R S E\left(\frac{x}{y}\right)=\sqrt{R S E(X)^{2}-R S E(Y)^{2}}\)

    Comparison of estimates

    7 Published estimates may also be used to calculate the difference between two survey estimates. Such an estimate is subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x-y) may be calculated by the following formula:

    \({SE}(x-y)=\sqrt{|S E(x)|^{2}+|S E(y)|^{2}}\)

    8 While the above formula will be exact only for differences between separate and uncorrelated (unrelated) characteristics of sub-populations, it is expected that it will provide a reasonable approximation for all differences likely to be of interest in this publication.

    9 Another measure is the Margin of Error (MOE), which describes the distance from the population value of the estimate at a given confidence level, and is specified at a given level of confidence. Confidence levels typically used are 90%, 95% and 99%. For example, at the 95% confidence level the MOE indicates that there are about 19 chances in 20 that the estimate will differ by less than the specified MOE from the population value (the figure obtained if all dwellings had been enumerated). The 95% MOE is calculated as 1.96 multiplied by the SE..

    10 The 95% MOE can also be calculated from the RSE by:

    \({MOE}(y) \approx \frac{R S E(y) * y}{100} * 1.96\)

    11 The MOEs in this publication are calculated at the 95% confidence level. This can easily be converted to a 90% confidence level by multiplying the MOE by

    \(\large\frac{1.645}{1.96}\)

    or to a 99% confidence level by multiplying by a factor of

    \(\large\frac{2.576}{1.96}\)

    12 A confidence interval expresses the sampling error as a range in which the population value is expected to lie at a given level of confidence. The confidence interval can easily be constructed from the MOE of the same level of confidence by taking the estimate plus or minus the MOE of the estimate.

    Example of interpretation of sampling error

    13 Standard errors can be calculated using the estimates and the corresponding RSEs. For example, the estimated proportion of males aged 18 years and over in New South Wales who are current daily smokers is 16.3%. The RSE for this estimate is 5.7%, and the SE is calculated by: 

    \(\begin{aligned} \text { SE of estimate } &=\left(\frac{R S E}{100}\right) \times estimate \\ &= 0.057 \times 16.3 \\ &=0.9 \end{aligned}\)

    14 Standard errors can also be calculated using the MOE. For example the MOE for the estimate of the proportion of males aged 18 years and over in New South Wales who are current daily smokers is +/- 1.8 percentage points. The SE is calculated by:

    \(\begin{aligned} \text { SE of estimate } &=\left(\frac{M O E}{1.96}\right) \\ &=\left(\frac{1.8}{1.96}\right) \\ &=0.9 \end{aligned}\)

    15 Note due to rounding the SE calculated from the RSE may be slightly different to the SE calculated from the MOE for the same estimate.

    16 There are about 19 chances in 20 that the estimate of the proportion of males aged 18 years and over in New South Wales who are currently daily smokers is within +/- 1.8 percentage points from the population value.

    17 Similarly, there are about 19 chances in 20 that the proportions of males aged 18 years and over in New South Wales who are currently daily smokers is within the confidence interval of 14.5% to 18.1%.

    Significance testing

    18 For comparing estimates between surveys or between populations within a survey it is useful to determine whether apparent differences are 'real' differences between the corresponding population characteristics or simply the product of differences between the survey samples. One way to examine this is to determine whether the difference between the estimates is statistically significant. This is done by calculating the standard error of the difference between two estimates (x and y) and using that to calculate the test statistic using the formula below:

    \(\large\frac{|x-y|}{S E(x-y)}\)

    19 If the value of the statistic is greater than 1.96 then we may say there is good evidence of a statistically significant difference at 95% confidence levels between the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations.

    Glossary

    The definitions used in this survey are not necessarily identical to those used for similar items in other collections. Additional information about the items is contained in the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001), including a more detailed Glossary.

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    Abbreviations

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