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A Test of How Australian Adults Allocate Time for Physical Activity

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A Test of How Australian Adults Allocate Time for Physical Activity
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  Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=vbmd20 Download by:  [118.208.170.251] Date:  14 September 2017, At: 00:03 Behavioral Medicine ISSN: 0896-4289 (Print) 1940-4026 (Online) Journal homepage: http://www.tandfonline.com/loi/vbmd20 A Test of How Australian Adults Allocate Time forPhysical Activity Amanda L. Rebar, Renee Johnston, Jessica L. Paterson, Camille E. Short,Stephanie Schoeppe & Corneel Vandelanotte To cite this article:  Amanda L. Rebar, Renee Johnston, Jessica L. Paterson, Camille E. Short,Stephanie Schoeppe & Corneel Vandelanotte (2017): A Test of How Australian Adults AllocateTime for Physical Activity, Behavioral Medicine, DOI: 10.1080/08964289.2017.1361902 To link to this article: http://dx.doi.org/10.1080/08964289.2017.1361902 Accepted author version posted online: 31 Jul 2017.Published online: 31 Jul 2017.Submit your article to this journal Article views: 32View related articles View Crossmark data  A Test of How Australian Adults Allocate Time for Physical Activity Amanda L. Rebar a , Renee Johnston a , Jessica L. Paterson b , Camille E. Short c , Stephanie Schoeppe a ,and Corneel Vandelanotte a a Physical Activity Research Group, School of Health, Medical, and Applied Sciences, Central Queensland University, Rockhampton, QLD,Australia;  b Appleton Institute, School of Health, Medical, and Applied Sciences, Central Queensland University, Adelaide, SA, Australia; c Freemasons Foundation Centre for Men ’ s Health; School of Medicine, University of Adelaide, Adelaide, SA, Australia ABSTRACT  The most common reported barrier to physical activity is a lack of suf  fi cient time. Just like mostresources in economics are  fi nite, so too is time within a day. We utilized a time-utility model tobetter understand how people are allocating time for physical activity. Additionally, we testedwhether the allocation of physical activity time impacts people ’ s perception of   “ lack of time ”  as abarrier for physical activity or their likelihood of being suf  fi ciently physical active. Australian adults(N  D  725 participants, 54% men) reported their time use throughout their day, perceived lack of time as a barrier to activity, and physical activity. Cluster analysis and  x 2 -tests were used to test thestudy research questions. People tended to either be entirely inactive (29%) or active while doingeither leisure (18%), occupation (18%), transport (14%), or household (22%) activities. Those whowere active during their leisure or transport time were most likely to be suf  fi ciently active. Therewere no signi fi cant differences among clusters in how much people perceived that lack of time wasa physical activity barrier. The commonly reported barrier of not having enough time to be activemight be a fallacy. Although a lack of time is a commonly reported barrier of physical activity, these fi ndings bring to light that increasing physical activity behavior is not as simple as adding moretime to the day. KEYWORDS exercise; barriers; leisuretime; motivation Introduction Regular engagement in physical activity yields numeroushealth bene fi ts including reduced risk of cardiovasculardisease, type 2 diabetes, depressive and anxiety symp-toms, and obesity. 1,2 More than half of all Australianadults are not engaging in the recommended 30 minutesof moderate physical activity on most days of the week,which is necessary to obtain these health bene fi ts. 3 Despite the common misperception that physical activity is a form of leisure (e.g., exercise), the recommendedamount of physical activity can also be accomplishedthrough occupation (e.g., lifting boxes), transportation(e.g., cycling) or household (e.g., gardening) activities.With more consideration for how people typically   fi tphysical activity in throughout their daily lives, we canmore effectively help people increase their physical activ-ity. It may be that people can become more active simply by trading sedentary for physical activity time throughsmall changes in daily life (e.g., parking further away from work, using a push lawn mower instead of ariding one).When people are asked to identify barriers that limitor prevent them from engaging in physical activity, they most of ten report the perception of having a lack of time. 4 – 6 Some evidence suggests that when people feeltime pressured, it is a consequence of their own choicesand perceptions, a phenomenon referred to as the  “ time-pressure illusion. ” 7 Australians spend 46% of their timedoing necessary activities (e.g., working, transportation)and 33% of their time doing contracted or committedactivities (e.g., household chores, voluntary work andcare), leaving a mere 21% of time for leisure. 8 This rela-tively little amount of free-time may contribute to thelow levels of physical activity people are engaging in.Only 43% of Australians are engaging in 30 minutes ormore of moderate intensity physical activity most days. 3 Time spent being physically active while in transporta-tion, working, and doing household chores has declinedover the past 50 years, a trend likely owing to environ-mental and technological advancements such as the shiftto suburb-living and the creation of inventions thatreduce the laborious requirements of the workforce and CONTACT  Amanda L. Rebar a.rebar@cqu.edu.au Physical Activity Research Group, School of Health, Medical, and Applied Sciences, Central QueenslandUniversity, Building 18 Rm 1.33, Rockhampton, QLD, Australia, 4703. © 2017 Taylor & Francis Group, LLC BEHAVIORAL MEDICINEhttps://doi.org/10.1080/08964289.2017.1361902    D  o  w  n   l  o  a   d  e   d   b  y   [   1   1   8 .   2   0   8 .   1   7   0 .   2   5   1   ]  a   t   0   0  :   0   3   1   4   S  e  p   t  e  m   b  e  r   2   0   1   7  household. 10 – 12 During these modern times of desk jobsand robot vacuums, people may feel that they have touse their leisure time as a means to be physically active.There are substantial health bene fi ts to be gained if peo-ple can  “ trade out ”  some of their time spent sitting withmore active behavior. For example, Ekblom-Bak and col-leagues found that replacing even small amounts of daily sitting with physical activity reduces the prevalence of metabolic syndrome. 13 Cawley contends that economic theoretical frame-works such as his SLOTH model can be utilized to offerinsight into how people allocate daily time to maximizetheir health. 14 Cawley described that similar to  fi nances,time is a  fi nite resource, limited by a series of constraints.People can  “ borrow, ” “ spend, ”  and  “ shift ”  the resource of time from one constraint to another, but each day thereis only 24 hours. In the SLOTH model,  S  represents timespent sleeping;  L  represents time spent in leisure;  O  rep-resents time spent in occupation;  T   represents time spentin transport; and  H   represents household/unpaidduties. 14 The total time of each day can be categorizedinto one of these domains. This time-budget SLOTHframework may be bene fi cial for understanding how people incorporate physical activity into their days, andfor putting into perspective the perceived barrier of nothaving enough time to be physically active.The purpose of this study is to utilize the SLOTHtime-utility model to re fl ect how people allocate time tophysical activity and to determine whether the way peo-ple allocate time to physical activity impacts their likeli-hood of being suf  fi ciently physically active or theirperception of time constraints as a barrier to physicalactivity. Additionally, we tested whether the way peopleallocated time to physical activity differed as a functionof how much they worked. These  fi ndings can informthe development of strategies and educational programsto promote physical activity not only as a leisure-timeactivity, but also as a means to accomplish other daily tasks as well. This may help people overcome one of thebiggest barriers to physical activity  — the perception of lack of time. 4 – 6 Methods From November – December 2014, Australian adults par-ticipated in a computer-assisted telephone interview.There was a strati fi ed randomization process to ensureequal numbers of male and female participants wererecruited. Geographically balanced landline sampleswere drawn to cover each state and territory. All study procedures were approved by the Central QueenslandUniversity Human Research Ethics Committee (Project:H14/09-203). Informed consent was obtained from allparticipants prior to the commencement of the inter- view. People with and without paid jobs have systemati-cally quite different obligations on their daily time. Fortime use analyses, these are quite unique populations,and as such should be studied independently. For thepurposes of this study, data were only used from partici-pants who reported working in a paid job on at least oneof the past seven days. Time spent in domain-speci  fi c physical activity   wasmeasured based on participants ’  self-reported responsesto questions from the SLOTH framework. 14 Participantswere asked how much time (in minutes) they spent on atypical workday and a typical non-workday in the lastweek in each of the SLOTH domains (sleep, leisure,occupation, transport, and household duties). Then, par-ticipants were asked how much time (in minutes) they spent being physically active on a typical workday and atypical non-workday in the last week in each of theSLOTH domains excluding sleep.Time spent in each domain was calculated as the timespent in a domain during a workday multiplied by the number of days worked in the past week, added withthe time spent in the same domain during non-workdaysmultiplied by the number of days not worked in the pastweek. Time spent being physically active was calculated inthe same manner. Therefore, the times can be interpretedas hours per week in each domain, taking into accountwork and non-workdays. The time spent being physically active in each domain was also calculated as a percentage.To assess whether participants were meeting physicalactivity recommendations,  physical activity level   wasdetermined based on the self-reported weekly sessionsand time spent engaging in walking, moderate activity,and vigorous activity via the Active Australia Survey. 15 The Active Australia Sur vey has shown good reliability and acceptable validity. 16 In line with national guidelinerecommendations, a nominal variable was calculated inwhich participants were categorized as  “ insuf  fi ciently active, ”  (physical activity  < 150 min or across fewer than fi  ve sessions) or  “ suf  fi ciently active ”  (physical activity   > 149 min across  fi  ve sessions or more). Perceived time constraint as a barrier to physical activ-ity   was measured based on participants ’  responses to asingle-item:  “ During this past week, how much did timeconstraints, or being busy, limit how much you couldengage in physical activity? ”  with the response optionsranging from 0 to 10 with anchors at 0 ( Not at All  ),5 (  Moderately  ), and 10 ( Very Much ). This item wasadapted from the relevant item from the validated self-ef  fi cacy for exercise scale. 17 The wording of the item wasrephrased so that the focus was on the perceived magni-tude of the barrier, as opposed to the degree of the belief that the barrier could be overcome. 2 A. L. REBAR ET AL.    D  o  w  n   l  o  a   d  e   d   b  y   [   1   1   8 .   2   0   8 .   1   7   0 .   2   5   1   ]  a   t   0   0  :   0   3   1   4   S  e  p   t  e  m   b  e  r   2   0   1   7  For descriptive purposes, participants were asked toself-report their gender ( “ male ”  or  “ female ” ), age (inyears), job status (i.e., full-time employed), and educa-tion level ( “ high school degree or less ”  or  “ advanceddegree ” ).To test for different pro fi les of how people were being physically active across the week, two-step cluster analy-sis 18,19 was conducted, which included percentage of time spent being physically active during the leisure,occupation, transportation, and household domains. Thesimilarity method of the Euclidean distance was used todetermine how many clusters were present based on thedistances of the hierarchical cluster dendogram andcomparisons of validity (i.e., Dunn internal validationindex and silhouette width), and stability (i.e., averageproportion of non-overlap [APN] and the average dis-tance [AD]) of alternative cluster solutions. 20 A  x 2 -test with false-discover rate adjusted  p -values toaccount for multiple tests 21 was used to test whetherthere were differences in how many people were suf  fi -ciently and insuf  fi ciently physically active among the dif-ferent clusters. Analysis of variance (ANOVA) was usedto test for among-cluster differences in the perception of time constraint as a barrier of physical activity and inhow many days per week people worked. All analyseswere conducted in  R  version 31.1.3. 22 Results The response rate for the survey was 29% (N  D  1349),which is representative of the general declining responserates of telephone surveys using landlines. 23 Of thosewho completed the survey, data were used from 725(54%) participants who reported working at least one of the past seven days in a paid job. (See Table 1 for partici-pants ’  characteristics and descriptive statistics.) In brief,participants were 46.80 § 14.80 years old. About half of the study sample worked 5 or 6 days a week (57%), weremale (54%), had a university degree or higher education(51%), and were suf  fi ciently physically active (54%). Onaverage, people reported a 4.80 ( SD  D  3.70) on a 1 – 10scale for how much they perceived time as a constraintto being physically active. Table 2 depicts the descriptivestatistics for the time spent in each domain and the pro-portion of time spent in physical activity per domain.People reported that more than half of their time spentdoing household duties was physically active (53%),while only around a third of their time spent doing lei-sure (30%) and occupational tasks (32%) and only 17%of their time spent in transport was active.The cluster analysis parceled the data into  fi  ve physi-cal activity time utility pro fi les. The  fi  ve-cluster result(Dunn Index  D  0.1, Silhouette D  0.3, APN D  0.4, AD D 0.8) had better internal validity and stability than thealternate four- (Dunn Index   D  0.0, Silhouette  D  0.3,APN D  0.4, AD D  0.9) and six- (Dunn Index  D  0.0, Sil-houette  D  0.3, APN  D  0.4, AD  D  0.7) cluster results.The average percentages of time being physically activeacross domains of the  fi  ve clusters are presented inFigure 1. The clusters represent A:  the active in leisure (87% of leisure time is spent being active), which repre-sented 18% of the sample; B:  the non-active  (no domainis made up of more than 12% physical activity), whichrepresented 29% of the sample; C:  the active transporters (91% of transport time is spent being active), which rep-resented 14% of the sample; D:  the active workers  (87%of occupation time is spent being active), which repre-sented 18% of the sample; and E:  the active in home (87% of time at home spent being active), which repre-sented 22% of the sample.When tested whether there were among-cluster differ-ences in the likelihood of being suf  fi ciently physically active, it was revealed that the active in leisure were morelikely to be suf  fi ciently active than the non-active (adjusted  p < .001), the active workers (adj.  p < .001), and the activein home (adj.  p <  .001). The active transporters were alsomore likely to be suf  fi ciently active compared to the non-active (adj.  p  <  .001), active workers (adj.  p  <  .001), and Table 1.  Participant characteristics and descriptive statistics. Age  M D 46.76  SD D 14.84Perceived time constraint to be active  M D 4.83  SD D 3.68Days of past week worked in paid job  n  %1 – 2 67 9.2%3 – 4 157 22.0%5 – 6 413 57.0%7 88 12.1%Gender  n  %Female 336 46.34Male 389 53.66Education  n  %High school completion or less 189 26.07Technical studies or further education 162 22.34University or higher education 370 51.03Physical Activity  n  %Suf  fi ciently active 392 54.07Insuf  fi ciently active 333 45.93 Table 2.  Time spent per domain and proportion of time spent inphysical activity per domain. Time Spent per Domain (Hours), M § SD Domain Weekly Per WorkdayPer Non-WeekdayProportion of Weekly DomainTimeSleep 50.08 § 11.21 7.17 § 3.71 7.46 § 2.70  — Leisure 18.93 § 15.89 1.88 § 2.50 4.71 § 4.57 0.30 § 0.35Occupation39.44 § 20.57 7.62 § 3.74 1.41 § 2.70 0.32 § 0.38Transport 9.52 §  9.89 1.58 § 3.03 1.21 § 1.55 0.17 § 0.32Household12.95 § 12.64 1.78 § 8.72 2.57 § 2.50 0.53 § 0.39 BEHAVIORAL MEDICINE 3    D  o  w  n   l  o  a   d  e   d   b  y   [   1   1   8 .   2   0   8 .   1   7   0 .   2   5   1   ]  a   t   0   0  :   0   3   1   4   S  e  p   t  e  m   b  e  r   2   0   1   7  the active in home (adj.  p <  .001). There were no signi fi -cant differences in how likely people were to be suf  fi ciently physically active between the active in leisure and activetransporters or among the non-active, active workers, oractive in home. There were no signi fi cant among-clusterdifferences in the perception of time constraint as a barrierto physical activity (F [4, 713] D 0.9,  p D 0.5). There were,however, signi fi cant differences among clusters for how many days per week they worked (F [4, 720]  D  4.4,  p <  .001). The active in home cluster tended to representpeople who worked fewer days than those represented by the active in leisure (  M   D  D ¡ 0.7, adj.  p <  .001) and theactive transport clusters (  M  D D¡ 0.6, adj.  p < .001). Discussion The aim of this study was to utilize Cawley  ’ s SLOTHtime utility model 14 to better understand how people uti-lize opportunities to be physically active. The results of this study showed that most people were inactive acrossall domains; however, there were groups of people thatspent the majority of either their leisure, occupational,transportation, or household time being physically active. Most commonly, people were making opportuni-ties to be active mostly in only one domain (i.e., eitherleisure, occupation, transportation, or household time).This  fi nding supports previous research suggesting thatpeople tend to either be active during occupational or lei-sure time — typically not both. 24,25 Sensibly, the  fi ndingsalso showed that those who worked more days per work were less likely to be active during their time at home.We also tested whether the way a person allocatedtheir time to physical activity was linked to their per-ceived barrier of lack of time for physical activity.Generally, people perceived time to be a barrier to physi-cal activity to only a modest degree, less than would beanticipated given the previous evidence suggesting lack of time is one of the most reported barriers to physicalactivity. 4 – 6 That people are inactive even if they feel thatthey do have suf  fi cient time to be active supports healthbehavior change research showing that increasing physi-cal activity is not as simple as  fi nding more time in theday. Rather, successful physical activity interventionswill likely require accounting for a complex, dynamicsystem of the regulatory processes at play in behaviorchange. 26,27 Our  fi ndings also showed that there were no signi fi -cant differences in how much people perceived time as abarrier to physical activity as a function of whether they were represented by the non-active, active in leisure,active in household time, active transporters, or active inoccupation cluster. That there were no signi fi cant differ-ences among these groups could re fl ect that a person ’ sperception of   “ lack of time ”  as a barrier for physicalactivity does not impact how they allocate time to activ-ity. Training people to engage in physical activity as ameans to achieve non-health related goals (e.g., biking for transportation, walking while socializing) could be aneffective way to overcome perceptions of a lack of timeand help people engage in and maintain physical activity.Alternatively, it may be that these  fi ndings re fl ect thesubjectivity of perceived time constraints. Perhaps by making people aware of times in which they could beactive but are choosing other less active activities (e.g.,watching television), people may become more activeduring times in their daily lives that allow for autono-mous behavior (e.g., leisure). Even small changes thatreplace sitting time with physical activity can have long-term health bene fi ts, so interventions targeting substitut-ing sitting for physical activity are likely worthwhile. 13 Those who were most likely to be suf  fi ciently physi-cally active mostly engaged in physical activity during their leisure or transportation time. These results are inline with previous research showing that the leisuredomain provides the greatest opportunity for physicalactivity, 28 and that transportation provides people addi-tional opportunities to engage in suf  fi cient physical activ-ity. 29 – 31 That those active during their occupational orhousehold activities were less likely to be suf  fi ciently active than those being active in leisure or transportcould re fl ect the bias of self-reported physical activity. 32 Measures such as the Active Australia Survey, 14 whichwas used in the present study, may overemphasize activ-ity conducted in the leisure and transport domains andunderrepresent occupational or household activity. 33 The examples provided within the questionnaire likely enhance recall of leisure and transport activities: Figure 1.  Percentage of time being physically active acrossdomains of leisure, occupation, transportation, and householdactivities for each of the  fi ve clusters: A: active in leisure, B: non-active, C: active transporters, D: active workers, and E: active inhome. 4 A. L. REBAR ET AL.    D  o  w  n   l  o  a   d  e   d   b  y   [   1   1   8 .   2   0   8 .   1   7   0 .   2   5   1   ]  a   t   0   0  :   0   3   1   4   S  e  p   t  e  m   b  e  r   2   0   1   7
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