YPMED-03949; No. of pages: 6; 4C: Preventive Medicine xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed

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Mitch J. Duncan a,⁎, Nicholas Gilson b, Corneel Vandelanotte a

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Available online xxxx

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Keywords: Sitting time CVD Risk awareness

Central Queensland University, Centre for Physical Activity Studies, Institute for Health and Social Science Research, Rockhampton, Australia The University of Queensland, School of Human Movement Studies, Brisbane, Australia

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Objective. Prolonged sitting is an emerging risk factor for poor health yet few studies have examined awareness of the risks associated with sitting behaviours. This study identifies the population subgroups with the highest levels of unawareness regarding the cardiovascular disease (CVD) risks associated with sitting behaviours. Method. Adults (n = 1256) living in Queensland, Australia completed a telephone-based survey in 2011, analysis conducted in 2013. The survey assessed participant's socio-demographic characteristics, physical activity, sitting behaviours and awareness of CVD risks associated with three sitting behaviours: 1) sitting for prolonged periods, 2), sitting for prolonged periods whilst also engaging in regular physical activity, and 3) breaking up periods of prolonged sitting with short activity breaks. Population sub-groups with the highest levels of unawareness were identified based on socio-demographic and behavioural characteristics using signal detection analysis. Results. Unawareness ranged from 23.3% to 67.0%. Age was the most important variable in differentiating awareness levels; younger adults had higher levels of unawareness. Body mass index, physical activity, TV viewing, employment status and time spent at work also identified population sub-groups. Conclusion. Unawareness of CVD risk for prolonged sitting was moderately high overall. Younger adults had high levels of unawareness on all of the outcomes examined. © 2014 Published by Elsevier Inc.

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Which population groups are most unaware of CVD risks associated with sitting time?☆

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Introduction

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Sitting is a key component of sedentary behaviour which is defined as any activity that has a metabolic cost less than 1.5 METS (Pate et al., 2008). Accumulating evidence indicates that prolonged sitting time may be associated with several poor health outcomes including cardiovascular disease (CVD) mortality (George et al., 2013; Patel et al., 2010; Pavey et al., 2012; Thorp et al., 2010; van der Ploeg et al., 2012). These associations remain evident following adjustment for a number of socio-demographic factors and health behaviours including physical activity (George et al., 2013; Patel et al., 2010; Pavey et al., 2012; Thorp et al., 2010; van der Ploeg et al., 2012). While sitting is required to perform some daily tasks and for rest, the adverse health consequences of sitting are driven by the prolonged and uninterrupted nature of sitting performed by many individuals. Sitting is a ubiquitous aspect of modern lifestyle, with many adults spending

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☆ Financial support: Population Research Laboratory, Institute for Health and Social Science Research, and CQUniversity. Duncan is supported by the National Heart Foundation Future Leader Fellowship (100029). This research was in part supported by the CQUniversity Health CRN. ⁎ Corresponding author at: CQUniversity, Centre for Physical Activity Studies, Institute for Health and Social Science Research, Bld 18, CQUniversity Australia, Rockhampton, Queensland 4702, Australia. Fax: +61 7 4930 6402. E-mail address: [email protected] (M.J. Duncan).

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between 7 and 9 h of their waking day sitting in work, travel or leisure contexts (Thorp et al., 2010; van der Ploeg et al., 2012). In light of this evidence, reducing the amount of prolonged sitting and increasing the amount of movement time are recommended to improve health outcomes (Owen et al., 2008). One factoring influencing an individual's decision to engage with an intervention or change behaviour is to acknowledge that they are at risk or participating in a risky behaviour (Schwarzer, 2008). The association between risk recognition and behaviour change has been documented in relation to numerous health behaviours (Brewer et al., 2007; Carpenter, 2010). Whilst knowledge and awareness of health risks associated with a behaviour are not sufficient to change behaviour alone, they are an important perquisite needed for change (Schwarzer, 2008). Given the evidence of health risks associated with long periods of sitting and its endemic occurrence in everyday activities it is important to identify those population groups who are most unaware of its health risks (Owen et al., 2008; Patel et al., 2010; Pavey et al., 2012; Thorp et al., 2010; van der Ploeg et al., 2012). This is highlighted by qualitative research in office workers that identified that increasing awareness of the health risks associated with sitting may be an important driver for changing sitting behaviours (Gilson et al., 2011). There is limited evidence of risk awareness related to sitting in the general population and these data are needed to inform future population level interventions directed at reducing sitting. Consequently this

http://dx.doi.org/10.1016/j.ypmed.2014.05.009 0091-7435/© 2014 Published by Elsevier Inc.

Please cite this article as: Duncan, M.J., et al., Which population groups are most unaware of CVD risks associated with sitting time?, Prev. Med. (2014), http://dx.doi.org/10.1016/j.ypmed.2014.05.009

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Data were obtained from a cross-sectional omnibus telephone survey, the Queensland Social Survey, conducted in July–August 2011 by the Population Research Laboratory of CQUniversity. Participants were adults aged 18 and over residing in the state of Queensland, Australia who were able to be contacted by direct dialled landline telephone. Participants were randomly selected from the electronic white pages (Scott and Happell, 2012). A minimum of five call-back attempts were made to a household if interviewers were unable to contact a participant. No data is available from individuals who declined to participate in the survey. The study was approved by CQUniversity's Human Research Ethics Committee and all participants consented to take part in the survey.

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Measures

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Participants reported socio-demographic details including age, gender, height, weight, smoking status, employment status, daily time spent at work, years of education completed, gross individual income (AUD/annum) and chronic disease status. Physical activity was assessed using the Active Australia Questionnaire, a valid and reliable instrument that asks participants to report the frequency and duration of walking for recreation and exercise, walking for transportation, and household (i.e. chores, yard work, gardening) and non-household (i.e. excluding household activities) activities of moderate or vigorous intensity in the last week (Australian Institute of Health and Welfare, 2003; Brown et al., 2004a, 2004b). This study used the format of this instrument that assesses walking for recreation and exercise and transport in two items (Brown et al., 2004a). Time spent in each of these activities (excluding household activities), with vigorous intensity weighted by 2, was summed to determine the total amount of time spent in moderate-to-vigorous intensity physical activity. The total reported frequency of participation in all activities was used to determine the total number of sessions for all reported activities, excluding household activities. Employed participants were asked to estimate how much time in total they spent sitting on an average working day using a single item. This item has been used in previous studies (Duncan et al., 2010; Mummery et al., 2005), and similar items have demonstrated excellent test retest reliability (Duncan et al., 2013). Duration of TV viewing in the previous week was assessed using a single item as a marker of sedentary behaviour in leisure time (Davies et al., 2012). Based on previously developed items (Badland and Duncan, 2009), three items assessing perceived CVD risk associated with sitting were developed specifically for the current study. The three items were: 1. “Sitting for long periods of time increases my risk of cardiovascular disease;” 2. “Even if I do regular physical activity, like brisk walking or exercise for 30 min most days of the week, sitting for long periods of time increases my risk of cardiovascular disease;” and 3. “When sitting for long periods of time, taking short breaks by standing or slowly moving around for a minute or two to break up my sitting is a good way to reduce my risk of cardiovascular disease.” Participants rated their level of agreement for these items on a five point scale ranging from ‘Strongly Agree’ to ‘Strongly Disagree’. Respondents could also choose a “don't know” response option. Individuals who agreed or strongly agreed with each item were classified as being aware of the risks associated with sitting; all other responses to each item were classified as being unaware of the risks. Being classified as unaware was the outcome of interest in the current study. A fourth outcome variable, individuals who were classified as unaware on at least one of the three risk awareness items, was created to provide an indicator of overall awareness.

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Signal Detection Analysis was used to identify the specific population subgroups that were unaware of the health risks posed by sitting behaviour (King et al., 2010; Vandelanotte et al., 2011). Signal Detection Analysis uses recursive partitioning in an iterative process to identify the optimal point in a predictor variable that classifies specific population subgroups who are at higher or lower risk of having the outcome (Kiernan et al., 2001). This process means that different cut points in a particular variable may be identified in analyses

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examining separate outcomes. Compared to logistic regression analysis which is commonly used to examine associations between predictor variables and the outcome, Signal Detection Analysis offers several advantages including being less sensitive to multicolinearity of predictor variables, systematically examining interactions between variables without needing to be specified a priori and the ability to control the false positive rate (Kiernan et al., 2001). A comparison of Signal Detection Analysis and logistic regression to identify subgroups can be found elsewhere (Kiernan et al., 2001). The subgroup partitioning process was set to maximize both sensitivity and specificity (50%) and used a p-value of 0.01 when identifying subgroups. Identification of population subgroups was conducted using a separate Signal Detection Model for each of the four outcomes using ROC 5.0 software (http://www.stanford.edu/ ~yesavage/ROC.html). Missing data was identified as missing as per the requirements of the software to avoid it being identified as a potential cut point in the analysis. Analysis was conducted in 2013. The thirteen predictor variables used in analysis were gender, age, employment status (employed; not employed), gross individual income (b $600/week; ≥$600/week), smoking status (current smoker, non-smoker), chronic disease status (present; absent), years of education, body mass index (BMI), daily time spent at work, daily duration of occupational sitting, weekly minutes of TV viewing, weekly minutes of physical activity and weekly sessions of physical activity. Individuals who reported not being employed were classified as having 0 min of daily time spent at work and 0 min of occupational sitting. Variables not specified as dichotomies were included in analyses as continuous measures. These predictor variables were selected as they may be useful in identifying subgroups who can be targeted in future interventions.

A total of 4009 people were invited to take part in the survey and a total of 1265 completed the survey; the response rate was 32%. The two most common reasons for not completing the survey were refusal to take part (n = 2309) and unable to be contacted (n = 276). Table 1 provides an overview of participant characteristics in the total sample and characteristics of those who were aware and unaware for each outcome variable. The proportion of the sample who were unaware of the risks associated with sitting for long periods, were unaware of the risk associated with sitting for long periods of time, even when they engaged in at least 30 min of moderate to vigorous intensity physical activity on a daily basis and were unaware that taking short breaks in sitting could reduce risk was 23.3%, 58.3% and 27.5% and respectively. Sixty-seven percent of participants were unaware of the risks associated with at least one of the sitting behaviours. The average age of the sample was 53.3 (SD = 16.0 years), the majority were employed, reported earning over $600/week and did not report the presence of a diagnosed chronic disease. For items 2, 3 and 4 participants who were unaware of the risks associated with sitting were significantly younger compared to those who were aware of the risks associated with sitting (Table 1). Specific variable thresholds identifying population sub-groups can be found in Figs. 1–4. For all analyses, age was the variable that most efficiently separated groups on levels of unawareness (Figs. 1–4). Following age, minutes of TV viewing, BMI, time spent engaged in physical activity and time spent at work were the variables that most efficiently separated sub-groups. Variables that further identified sub-groups with the lowest and highest levels of unawareness were BMI, smoking status, minutes of TV viewing, age, minutes and sessions of physical activity and employment status. Fig. 1 shows that overall 23.3% of the sample was unaware of the risks associated with long periods of sitting. The sub-group with the highest level of unawareness were those aged b37 years who watched ≥900 minutes of TV in the previous week (46.2%). The sub-group with the lowest level of unawareness were those aged ≥ 37 years, who watched ≥ 300 min of TV in the previous week and who were nonsmokers (18.5%). Overall the level of unawareness regarding the risks associated with sitting even when engaging in physical activity was 58.4% (Fig. 2). The sub-group with the highest level of unawareness were those aged b48 years, who reported ≥253 min of physical activity and ≥120 min

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study aims to identify population subgroups that have the highest levels of unawareness regarding the potential for increased health risk associated with prolonged and uninterrupted sitting.

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M.J. Duncan et al. / Preventive Medicine xxx (2014) xxx–xxx Table 1 Participant socio-demographic and behavioural characteristics of the overall sample and by different levels of awareness, in Queensland adults, 2011.a Q1b

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53.3 (16.0)

295 (23.3) 51.86 (17.6)

969 (76.6) 53.71 (15.5)

737 (58.3) 51.9 (16.1)⁎

526 (41.6) 55.3 (15.8)

633 (50.0) 632 (50.0)

151 (23.9) 144 (22.8)

481 (76.0) 488 (77.2)

366 (57.8) 371 (58.7)

266 (42.0) 260 (41.1)

Employment status Employed Not Employed

532 (42.1) 731 (57.8)

124 (23.3) 171 (23.4)

408 (76.7) 559 (76.5)

293 (55.1) 442 (60.5)

Individual income b$600/week ≥$600/week

562 (44.4) 703 (55.6)

176 (25.0) 119 (21.2)

526 (74.8) 443 (78.8)

Smoking status Current smoker Non smoker

172 (13.6) 1092 (86.4)

52 (30.2) 243 (22.3)

Chronic disease status Present Absent BMI Years of education Min. of work/day Daily min. of occupational sitting Weekly min. of TV viewing time Weekly min. of physical activity Weekly sessions of physical activity

563 (44.5) 702 (55.5) 27.2 (5.5) 13.5 (3.53) 278.5 (260.0) 132.3 (181.3) 817.9 (611.6) 298.9 (329.2) 6.3 (11.2)

125 (22.2) 170 (24.2) 26.9 (5.3) 13.3 (3.5) 289.8 (271.3) 129.5 (185.7) 815.2 (676.2) 310.1 (330.8) 6.7 (13.9)

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M (SD) n (%)

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347 (27.4) 51.2 (16.3)⁎

917 (72.5) 54.0 (15.9)

848 (67.0) 51.9 (16.2)⁎

417 (33.0) 56.1 (15.4)

178 (28.1) 169 (26.7)

454 (71.7) 463 (73.3)

418 (66.0) 430 (68.0)

215 (34.0) 202 (32.0)

238 (44.7) 288 (39.4)

144 (27.1) 203 (27.8)

387 (72.7) 528 (72.2)

345 (64.8) 501 (68.5)

187 (35.2) 230 (31.5)

408 (58.0) 329 (58.5)

293 (41.7) 233 (41.5)

198 (28.2) 149 (26.5)

504 (71.7) 413 (73.5)

474 (67.4) 374 (66.5)

229 (32.6) 188 (33.5)

120 (69.8) 848 (77.7)

104 (60.5) 633 (58.0)

68 (39.5) 457 (41.8)

53 (30.8) 294 (26.9)

119 (69.2) 797 (73.0)

118 (68.6) 730 (66.8)

54 (31.4) 362 (33.2)

438 (77.8) 531 (75.6) 27.3 (5.5) 13.5 (3.5) 274.7 (256.2) 132.8 (179.6) 818.5 (591.1) 295.3 (329.0) 6.1 (10.2)

319 (56.7) 418 (59.5) 27.1 (5.6) 13.5 (3.5) 290.5 (259.0) 135.5 (179.4) 813.7 (617.4) 300.8 (320.6) 6.5 (13.3)

243 (43.2) 283 (40.3) 27.4 (5.2) 13.4 (3.6) 262.1 (260.8) 128.1 (184.2) 824.5 (604.8) 296.7 (341.6) 5.9 (7.2)

405 (71.9) 512 (72.9) 27.3 (5.4) 13.4 (3.6) 277.5 (258.3) 133.2 (180.8) 813.1 (590.6) 304.3 (330.3) 6.5 (12.0)

370 (65.7) 478 (68.1) 27.1 (5.8) 13.6 (3.5) 286.7 (261.6) 135.7 (182.4) 812.3 (611.5) 295.2 (315.9) 6.3 (12.5)

193 (34.3) 224 (31.9) 27.4 (4.9) 13.3 (3.6) 261.9 (256.1) 125.5 (179.0) 829.4 (612.3) 306.4 (355.2) 6.2 (7.8)

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157 (27.9) 190 (27.1) 27.0 (5.7) 13.8 (3.4) 282.0 (264.5) 130.5 (183.0) 830.6 (665.5) 285.3 (326.6) 5.8 (8.4)

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of TV in the previous week (75.8%). The sub-group with the lowest level of unawareness were aged ≥48 years and had a BMI ≥27.4 (48.6%). Approximately 27% of participants were unaware that interrupting periods of sitting by standing or walking slowly could reduce CVD risk (Fig. 3). Those aged ≥ 50 years with a BMI b23.7 and who were employed were the sub-group with the highest level of unawareness (40.9%). Those aged ≥ 50 years who reported a BMI b23.7 and were not employed were the sub-group with the lowest level of unawareness (16.7%). Fig. 4 shows that 67.0% of participants were classified as unaware on at least one of the statements. Participants aged b 50 years and who

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⁎ Significant differences between aware and unaware groups. p ≤ 0.05. a Number of participants vary due to missing data on socio-demographic and behavioural variables. b Sitting for long periods of time increases my risk of cardiovascular disease. c Even if I do regular physical activity, like brisk walking or exercise for 30 min most days of the week, sitting for long periods of time increases my risk of cardiovascular disease. d When sitting for long periods of time, taking short breaks by standing or slowly moving around for a minute or two to break up my sitting is a good way to reduce my risk of cardiovascular disease. e The proportion of people who were classified as unaware on at least one of the three awareness items (Q1–Q3).

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reported watching ≥120 min of TV viewing in the previous week had the highest level of unawareness to at least one of the statements (77.1%). Those participants aged ≥50 years with a BMI between 27.4 and 29.4 had the lowest level of unawareness (45.0%).

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This study examined the prevalence of being unaware of the CVD risks associated with sitting behaviour and used socio-demographic and behavioural variables to identify the population sub-groups who had the lowest and highest prevalence of unawareness. The current

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Fig. 1. Hierarchy of predictors for being unaware that sitting for long periods of time increase risk of CVD in Queensland adults, 2011.

Please cite this article as: Duncan, M.J., et al., Which population groups are most unaware of CVD risks associated with sitting time?, Prev. Med. (2014), http://dx.doi.org/10.1016/j.ypmed.2014.05.009

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of knowledge regarding CVD risk factors (Potvin et al., 2000; Stroebele et al., 2011); although the older adults referred to in these studies were older (e.g. N 65 years or N75 years) than thresholds identified in the current study. Figs. 2, 3, and 4 identified people aged less than approximately 50 as one potential socio-demographic group for targeting in interventions addressing risks associated with sitting. Additionally, Fig. 1, identified those aged 18 to 36 years as a group with high levels of unawareness compared to their older counterparts. Although these age ranges are broad this information may be useful in guiding the style or approach of future interventions. Previous studies have identified that women and individuals with greater levels of education have increased awareness of risk factors for conditions such as stroke and CVD (Gans et al., 1999; Potvin et al., 2000; Stroebele et al., 2011). These socio-demographic factors were not among the variables that identified population subgroups in the current study. This may be related to the relatively consistent level of unawareness across these population groups. Alternatively it may be possible that other factors such as employment status and engagement in physical activity may be more important drivers of risk awareness in this population. In line with the current study, qualitative research indicates that population sub-groups differ in awareness of different risks associated with sitting (Gilson et al., 2011, 2012). For example, office workers reported high levels of awareness of the musculoskeletal risks associated

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findings demonstrate that over two thirds of the adult population is unaware of the risks associated with at least one of the sitting behaviours examined. Study outcomes also provide insight on the particular sub-groups who had the highest levels of unawareness. Given that awareness is one of the necessary pre-requisites for behaviour change (Brewer et al., 2007; Schwarzer, 2008), it is likely that awareness raising initiatives will be a useful component of strategies to reduce sitting, particularly in those groups with high levels of unawareness. Of the three individual perceived risk items assessed, unawareness was highest for the item examining CVD risk associated with sitting even when engaging in physical activity (58.4%). Accumulating evidence indicates that longer durations of sedentary behaviour increase CVD risk factors (waist circumference, triglycerides, insulin sensitivity) when statistically adjusting for minutes of moderate-to-vigorous intensity physical activity (Thorp et al., 2010). In light of the high levels of unawareness and as evidence on the adverse impacts of sitting accumulates it will be important to highlight the importance of engaging in both moderate-to-vigorous intensity physical activity and reduced sitting time (Owen et al., 2008). For all four outcomes, age was the most important variable that differentiated population sub-groups. Individuals in the younger age groups had higher levels of unawareness compared to older age groups. This is both in agreement and contrast with previous studies in which older adults report both higher (Mosca et al., 2010) and lower levels

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Fig. 2. Hierarchy of predictors for being unaware that sitting for long periods of time increase risk of CVD even if regular physical activity is performed in Queensland adults, 2011.

Fig. 3. Hierarchy of predictors for being unaware that taking breaks during long periods of sitting reduces risk of CVD, in Queensland adults, 2011.

Please cite this article as: Duncan, M.J., et al., Which population groups are most unaware of CVD risks associated with sitting time?, Prev. Med. (2014), http://dx.doi.org/10.1016/j.ypmed.2014.05.009

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Conflict of interest statement The authors declare that there are no conflicts of interests.

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The authors would like to thank the Population Research Laboratory (PRL) for the data collection and Ms Christine Hanley manager of the PRL for her supervision of the data collection process and assistance in formulating the questions.

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References

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Australian Institute of Health and Welfare, 2003. The Active Australia Survey: A Guide and Manual for Implementation, Analysis and Reporting. , AIHW, Canberra. Badland, H.M., Duncan, M.J., 2009. Perceptions of air pollution during the work-related commute by adults in Queensland, Australia. Atmos. Environ. 43, 579–5795. Brewer, N.T., Chapman, G.B., Gibbons, F.X., Gerrard, M., McCaul, K.D., Weinstein, N.D., 2007. Meta-analysis of the relationship between risk perception and health behavior: the example of vaccination. Health Psychol. 26, 136–145. Brown, W.J., Bauman, A., Chey, T., Trost, S., Mummery, K., 2004a. Comparison of surveys used to measure physical activity. Aust. N. Z. J. Public Health 28, 128–134. Brown, W.J., Bauman, A., Trost, S., Mummery, W.K., Owen, N., 2004b. Test–retest reliability of four physical activity measures used in population surveys. J. Sci. Med. Sport 7, 205–215. Carpenter, C.J., 2010. A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun. 25, 661–669. Davies, C.A., Vandelanotte, C., Duncan, M.J., van Uffelen, J.G., 2012. Associations of physical activity and screen-time on health related quality of life in adults. Prev. Med. 55, 46–49. Di Milia, L., Vandelanotte, C., Duncan, M.J., 2013. The association between short sleep and obesity after controlling for demographic, lifestyle, work and health related factors. Sleep Med. 14, 319–323. Duncan, M.J., Badland, H.M., Mummery, W.K., 2010. Physical activity levels by occupational category in non-metropolitan Australian adults. J. Phys. Act. Health 7, 718–723. Duncan, M.J., Rashid, M., Vandelanotte, C., Cutumisu, N., Plotnikoff, R.C., 2013. Development and reliability testing of a self-report instrument to measure the office layout as a correlate of occupational sitting. Int. J. Behav. Nutr. Phys. Act. 10, 16.

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called and the response rate was lower than reported in other surveys (Di Milia et al., 2013; Hu et al., 2011), these factors should be considered when interpreting outcomes. The strengths of the study were the sample size and that survey respondents were randomly drawn from the general population in a wide geographic area. Replication of this study in different locations and in different populations will assist in understanding the generalizability of study outcomes. In conclusion, the findings highlight that the level of risk unawareness associated with sitting is moderately high, with younger adults consistently identified as having higher levels of unawareness. This level of unawareness emphasizes the importance of campaigns to raise the awareness of risks associated with sitting as one component of future interventions.

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with sitting and lower levels of awareness in relation to CVD risks (Gilson et al., 2011). While another study indicated that occupational health and safety professionals reported high levels of both musculoskeletal and CVD risks associated with sitting and the risks posed by replacing sitting with long periods of standing due to increased risk of vascular conditions and other disorders associated with long periods of standing (Gilson et al., 2011). Fig. 3 shows that employed persons had the highest levels of unawareness related to breaks in sitting time indicating this group may benefit from interventions addressing this issue as part of a broader intervention approach. However, such interventions would need to address broader issues identified in workplaces that impact on the likelihood to take breaks such as social norms and job autonomy (Gilson et al., 2011). TV viewing is a highly prevalent leisure time sedentary activity (King et al., 2010; Thorp et al., 2010) and King et al. (King et al., 2010) identified that TV or DVD based physical activity promotion programs may be a useful to reach population sub-groups who watch high levels of TV. In the current study the amount of time spent watching TV consistently identified population sub-groups that were unaware of the CVD health risks associated with sitting time. Although many people spend time watching TV, the variation in TV viewing cut-points and demographic factors observed in this study is likely to impact the effectiveness of broad TV based campaigns. TV advertisement placement can be tailored based on broad demographic characteristics likely to watch a program which when paired with outcomes of this study could be used to better target specific groups for TV based campaigns. Internet use is likely to increase as accessibility and the availability of mobile devices continues to increase. As a result internet and mobile device based campaigns may also be a useful strategy to reach large populations groups including population sub-groups identified in this research. Limitations of the current study include the cross sectional nature of the data collected meaning that causality regarding awareness and sitting behaviour cannot be determined, the absence of a measure of participant ethnicity, and the lack of a measure of total sitting time in all domains of life. Incorporating measures of sitting in multiple domains may have provided greater insight regarding how sitting time influenced levels of risk awareness by allowing comparisons of unawareness by different levels of sitting behaviour. Such comparisons would allow identification of those specific groups that were both unaware and engaged in higher levels of sitting which may be more useful for targeting interventions. The items assessing unawareness were intended to assess awareness of risks associated with sitting if the respondent was to engage in the behaviour in the way described rather than participant’s perceived personal level of risk. Yet some participants may have responded based on their perceived personal level of risk, this is a potential limitation of the study. Only land line telephones were

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Fig. 4. Hierarchy of predictors for being unaware for at least one of the three statements, in Queensland adults, 2011.

Please cite this article as: Duncan, M.J., et al., Which population groups are most unaware of CVD risks associated with sitting time?, Prev. Med. (2014), http://dx.doi.org/10.1016/j.ypmed.2014.05.009

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Which population groups are most unaware of CVD risks associated with sitting time?

Prolonged sitting is an emerging risk factor for poor health yet few studies have examined awareness of the risks associated with sitting behaviours. ...
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