Measuring physical inactivity:do current measures provide an accurate view of "sedentary" video game time?

BACKGROUND: Measures of screen time are often used to assess sedentary behaviour. Participation in activity-based video games (exergames) can contribute to estimates of screen time, as current practices of measuring it do not consider the growing evidence that playing exergames can provide light to moderate levels of physical activity. This study aimed to determine what proportion of time spent playing video games was actually spent playing exergames. METHODS: Data were collected via a cross-sectional telephone survey in South Australia. Participants aged 18 years and above (n = 2026) were asked about their video game habits, as well as demographic and socioeconomic factors. In cases where children were in the household, the video game habits of a randomly selected child were also questioned. RESULTS: Overall, 31.3% of adults and 79.9% of children spend at least some time playing video games. Of these, 24.1% of adults and 42.1% of children play exergames, with these types of games accounting for a third of all time that adults spend playing video games and nearly 20% of children's video game time. CONCLUSIONS: A substantial proportion of time that would usually be classified as "sedentary" may actually be spent participating in light to moderate physical activity

Surveillance of health status and health risk: The future of data collection using the telephone in Australia

Epidemiologically-designed, continuous and effective chronic disease and behavioural risk factor surveillance systems provide scientific evidence at the local level to assist government, health professionals and administrators, to respond effectively in reducing the burden associated with non-communicable diseases (NCDs). Many monitoring and surveillance systems utilise the telephone as the method of choice in obtaining population data. However, the dramatic change in telecommunication usage, diminishing coverage of telephone sampling frames and declining participation in household surveys, has led to methodological and statistical challenges. This has led to the present study that explores these challenges through an established telephone data collection system in Australia, the South Australian Monitoring and Surveillance System (SAMSS). The aim of this research is to determine how telephone surveys in Australia can continue to be used to reliably collect representative information on health indicators and other related health issues by exploring alternative efficient and cost effective methods. The first study, using face-to-face South Australian household survey data, found that using landline-based telephone number sampling frames excludes mobile-only households in Australia (27.8% of households in 2013). From 2006 to 2013, the proportion of mobile-only households has increased and this trend does not appear to be plateauing. This corresponds with the decrease in landline telephone coverage. Mobile-only households are demographically different in that respondents are more likely to be younger, never married and living in rented accommodation. By excluding this group, landline-based sampling frames may possibly produce biased health estimates for some health indicators, such as the proportion of people who are current smokers or who have a mental health condition. The second study found participation in SAMSS has decreased over a period of twelve years, with an 18.6% decrease in the response rate (from 68.9% in 2002 to 56.1% in 2014) and a 65.5% increase in the refusal rate. When demographic data are compared to Census data, SAMSS had a higher proportion of females, older people and people who rent, and these groups are increasingly being over-represented over time. The result from these studies imply that a mobile telephone sample needs to be incorporated. Unfortunately, there is no complete mobile telephone sampling frame in Australia with a geographical marker and only 7% of the currently used nationwide mobile telephone sampling frames are South Australian residents, making the sampling method uneconomic. This is compounded by lower participation in mobile telephone surveys compared to landline telephone surveys. Based on these methodological issues and corresponding with decline in participation, efficient methodological strategies need to be considered for smaller states like South Australia. The last two studies present two different cost effective and efficient methodological techniques, to minimise bias in health estimates due to nonresponse and sample coverage, and to increase participation in mobile telephone surveys. One study used raked weighting methodology to overcome, to some extent, the nonresponse biases and sampling coverage problems associated with telephone surveys. By incorporating more sociodemographic variables such as renting and marital status, besides the usual age, sex and area of residence, health estimates such as the proportion of current smokers corresponds well with other more expensive face-to-face surveys. The last study used a simple novel technique of sending a text message to prospective survey respondents to improve participation. This thesis has explored and shown, from a series of studies, that telephone surveys, with careful monitoring of procedures and use of innovative techniques and statistical methods, can still be used to collect and report information on chronic diseases and behavioural risk factors in Australia. The uniqueness of this body of works presents a detailed examination of the status of a current surveillance system by nonresponse rates, trends of nonresponse rates and coverage biases, and links this information to possible solutions to overcome nonresponse biases, with the aim of producing reliable and representative health estimates.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 201

Measuring subjective wellbeing in a surveillance system: ¿who are these people who are positive?

Background: Promo ng and improving the wellbeing (and happiness) of the general popula on is emerging as a public health priority globally and a key societal aspira on. Countries are including measures on wellbeing to supplement tradi onal measures of economic growth as an indicator of na onal prosperity. Including ques ons on subjec ve individual wellbeing can be useful to measure and can be included in a surveillance system. These ques ons can provide an overall assessment of how people are doing and take into account of what people decide for themselves is important beside health. It can capture the meaning and purpose in life which is not covered in current “nega ve” ques ons such as psychological distress. Purpose: To assess the performance and associa on of subjec  ve wellbeing on a range of socio- economic status (SES) and inequali  es indicators

The independent association of overweight and obesity with breathlessness in adults: a cross-sectional, population-based study

Obesity is an independent risk factor for chronic breathlessness and should be assessed in people with this symptom

The impact of socioeconomic status on arthritis and osteoporosis

Background: Low socioeconomic status has been shown to be associated with both osteoarthri s and rheumatoid arthri s, impac ng on outcomes and even the development of arthri s. However the associa on with osteoporosis has been less clear. The reasons for the associa on may be linked to socioeconomic status through educa onal aspects, income, employment type and even area of residence. Purpose: The purpose of this study was use a monthly surveillance and monitoring system to examine the prevalence of self-reported osteoarthri s, rheumatoid arthri  s and osteoporosis over  me and the associa  on with measures of socioeconomic status

Measuring social capital in a known disadvantaged urban community – health policy implications

BACKGROUND: To assess the social capital profile of a known disadvantaged area a large cross-sectional survey was undertaken. The social capital profile of this area was compared to data from the whole of the state. The overall health status of the disadvantaged area was assessed in relation to a wide variety of social capital related variables. Univariate and multivariate analysis were undertaken. RESULTS: In the univariate analysis many statistically significant differences were found between the respondents in the disadvantaged area and the state estimates including overall health status, perceived attributes of the neighbourhood, levels of trust, community involvement and social activities. In the multivariate analysis very few variables were found to be statistically significantly associated with poorer health status. The variables that jointly predicted poorer health status in the disadvantaged area were older age, lower income, low sport participation, non-seeking help from neighbours and non-attendance at public meetings. CONCLUSION: Measuring social capital on a population level is complex and the use of epidemiologically-based population surveys does not produce overly valuable results. The inter-relational/dependence dichotomy of social capital is not yet fully understood making meaningful measurement in the broader population extremely difficult and hence is of questionable value for policy decision making

The use of a chronic disease and risk factor surveillance system to determine the age, period and cohort effects on the prevalence of obesity and diabetes in South Australian adults--2003-2013

Age, period and cohort (APC) analyses, using representative, population-based descriptive data, provide additional understanding behind increased prevalence rates

Public attitudes to government intervention to regulate food advertising, especially to children

The World Health Organization has called on governments to implement recommendations on the marketing of foods and beverages to children. This study describes high public support for government intervention in marketing of unhealthy food to children and suggests more effort is needed to harness public opinion to influence policy development

Increasing gaps in health inequalities related to non-communicable diseases in South Australia; implications towards behavioural risk factor surveillance systems to provide evidence for action

Although Australia is a country cited as having generally low health inequalities among different socioeconomic groups, inequalities have persisted. The aim of this analysis was to highlight how inequalities have evolved over a 13 years period in South Australia (SA)

Health Estimates Using Survey Raked-Weighting Techniques in an Australian Population Health Surveillance System

A challenge for population health surveillance systems using telephone methodologies is to maintain representative estimates as response rates decrease. Raked weighting, rather than conventional poststratification methodologies, has been developed to improve representativeness of estimates produced from telephone-based surveillance systems by incorporating a wider range of sociodemographic variables using an iterative proportional fitting process. This study examines this alternative weighting methodology with the monthly South Australian population health surveillance system report of randomly selected people of all ages in 2013 (n = 7,193) using computer-assisted telephone interviewing. Poststratification weighting used age groups, sex, and area of residence. Raked weights included an additional 6 variables: dwelling status, number of people in household, country of birth, marital status, educational level, and highest employment status. Most prevalence estimates (e.g., diabetes and asthma) did not change when raked weights were applied. Estimates that changed by at least 2 percentage points (e.g., tobacco smoking and mental health conditions) were associated with socioeconomic circumstances, such as dwelling status, which were included in the raked-weighting methodology. Raking methodology has overcome, to some extent, nonresponse bias associated with the sampling methodology by incorporating lower socioeconomic groups and those who are routinely not participating in population surveys into the weighting formula