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The short-term effects of a mass reach physical activity campaign: an evaluation using hierarchy of effects… Berry, T. R; Rhodes, R. E; Ori, E. M; McFadden, K.; Faulkner, G.; Latimer-Cheung, A. E; O’Reilly, N.; Spence, J. C; Tremblay, M. S; Vanderloo, L. M Nov 27, 2018

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RESEARCH ARTICLE Open AccessThe short-term effects of a mass reachphysical activity campaign: an evaluationusing hierarchy of effects model andintention profilesT. R. Berry1*, R. E. Rhodes2, E. M. Ori1, K. McFadden1, G. Faulkner3, A. E. Latimer-Cheung4, N. O’Reilly5, J. C. Spence1,M. S. Tremblay6 and L. M. Vanderloo7AbstractBackground: Mass reach physical activity campaigns are designed to deliver physical-activity related messages to alarge population across different media including print, television, radio, and websites. Few evaluations haveexamined the short-term effects of a mass reach campaign on participants who were engaged with the campaign.The current research examined the short-term effects of the ParticipACTION 150 Play List, a mass reach physicalactivity campaign, on participants who registered with the campaign website.Methods: Participants (N = 7801) completed a registration questionnaire measuring demographic information,awareness and recall of physical activity and sport advertising, and self-reported number of activities tried orplanned to try from the 150 Play List. A follow-up survey was completed by 1298 participants from the originalsample. Additional questions assessed experience with the 150 Play List and attitudes towards campaignadvertisements.Results: Approximately 14.5% of participants cited the ParticipACTION 150 Play List and 23.6% mentioned a ‘gettingactive’ message when recalling advertisements. Those who named the 150 Play List or getting active reportedmore activities tried and more activities planned than those who did not. They were also more likely to say theyhad tried a new activity and planned ongoing participation. It was also found that participants with a disabilitywere more likely to have tried a new activity compared to those not in a minority group. Other correlates of tryingnew activities at follow-up were younger age, more positive reported experience with the 150 Play List, and morefavourable attitudes towards campaign advertisements. Those who did not intend continued participation, or whowere unsure at baseline and then decided against continued participation at follow-up, reported they were lesssedentary or encouraging others to be active.Conclusions: This research addresses the gap in evidence regarding the efficacy of mass reach physical activitycampaigns by informing whether a year-long campaign like the 150 Play List can be effective in influencing thebehavior of those engaged with the campaign. The results reinforce the idea that ‘top of mind’ awareness shouldbe measured. Investigating intention profiles can help inform campaign impacts and continuation intentions.Keywords: Physical activity, Mass media campaigns, Evaluation, Hierarchy of effects, Intention profiles, Post-campaign effects* Correspondence: tanyab@ualberta.ca1Faculty of Kinesiology, Sport, and Recreation, 1-153 University Hall;University of Alberta, T6G 2H9, Edmonton, AB 780 492 3280, CanadaFull list of author information is available at the end of the article© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Berry et al. BMC Public Health         (2018) 18:1300 https://doi.org/10.1186/s12889-018-6218-7BackgroundMass reach physical activity (PA) campaigns are de-signed to deliver PA-related messages to a large popula-tion across different media including print, television,radio and websites. A review indicates that most evalua-tions of such campaigns measure intentions rather thanbehaviors as the outcome [1]. Others have similarlyhighlighted that short-term outcomes, such as changesin attitudes and intentions, are more often measured [2].Most evaluation studies included in these reviews exam-ined campaign effects across a population, such as howmany people in the target audience were aware of thecampaign and subsequently affected by it. Few evalua-tions have examined the effects of a mass reach cam-paign on participants who were engaged with thecampaign; in other words, whether PA participation thatcan be in part attributed to the campaign, is maintained.The current research examined the short-term effects ofthe ParticipACTION 150 Play List, a mass reach PAcampaign (referred to as the 150 Play List hereafter), onparticipants who registered with the campaign website.ParticipACTION, a Canadian, national not-for-profitorganization with the mandate of promoting PA, was se-lected by Heritage Canada (the Canadian governmentaldepartment responsible for policies and programs regard-ing the arts, culture, media, and sports) to create one ofthe signature initiatives in celebration of Canada’s 150thbirthday in 2017. The resulting campaign was the 150 PlayList [3]. In its first phase (October 12 – December 9,2016), Canadians were invited to suggest physical activ-ities, that any person might ostensibly try, for possible in-clusion. These activities, both sport (e.g., ball hockey,sitting volleyball) and non-sport (e.g., walking, gardening),were voted on by the Canadian public. ParticipACTIONcreated a final list of 150 activities that reflected the mostpopular activities, while also ensuring activity diversityand inclusivity (i.e., all activities had to be suitable foradaptation for people with a sensory, intellectual, or mo-bility disability). In addition, seven traditional Indigenoussports (e.g., knuckle hop) were included. The 150 Play Listwas launched in January 2017 and each of the final 150 ac-tivities had a feature page on the campaign website. Parti-cipACTION also hosted 100 community events acrossCanada throughout the year with the intent of attractingCanadians to try some or all of the activities. The websitewas produced in both English and French and communityevents were hosted in both languages. Canadians, aged≥13 years, could track their completed activities on thewebsite and be eligible for prizes. There were campaignadvertisements on national television and radio, and socialmedia. In addition, a media sponsor profiled activities andevents during news broadcasts.The hierarchy of effects model (HOEM) [4] was used toguide this evaluation of the 150 Play List. The HOEMproposes that evaluations of mass reach campaigns shouldfirst establish if there are immediate effects, such asawareness of the campaign, prior to evaluating more distalgoals including changes in attitudes, self-efficacy, or inten-tions, and ultimately behavior [4]. Intuitively, engagingwith a program component would suggest awareness (e.g.,registering as a program participant). However, researchhas found that among participants who registered on a PAcampaign website, those who named the campaign with-out prompting (only 59% of the study sample) reportedhigher attentional bias toward campaign logos and higherself-reported PA compared to those who did not name thecampaign. [5]. As these authors argue, not naming thecampaign by website registrants indicates that campaignevaluators should recognize that people registered with acampaign may not actually be thinking much about it.Thus, the current research similarly assessed ‘top of mind’campaign awareness among campaign registrants, inaddition to other HOEM variables: attitudes, self-efficacy,and intentions, and behavioral outcomes such as tryingnew activities. The HOEM has been used to examinepopulation effects across many mass reach campaigns (cf.[1, 2]) including others from ParticipACTION [6, 7], al-though some studies with other campaigns have foundlimited support for the sequence of effects outlined in themodel [8]. Regardless of whether the cascade of effects oc-curs in sequence (e.g., change in attitudes precedes behav-ior change), using the HOEM can guide evaluation ofdifferent levels of effects.In addition to the HOEM, the current research also ex-amined campaign effects across profiles of non-intenders(those who have no intention of continued participation in150 Play List activities), ambivalent intenders (those whoare unsure if they will continue participation), and intenders(those who intend to continue to participate). This is anovel contribution to evaluation research because it ad-dresses the intention-behavior gap, where roughly half of allpeople with positive intentions to engage in PA do not acton them [9]. The separation of intention-behavior profilesis known as an action control framework, and is useful forexploring the predictors of intention formation and thetranslation of intentions into behavior [10]. The current re-search focused on intention to continue with 150 Play Listactivities, as these continuation intentions are an importantdeterminant of maintained behavior [11].Therefore, the purpose of this research was to investi-gate the short-term effects of the 150 Play List amongparticipants who registered for the campaign using theHOEM as a guide and across different intention profiles.MethodsParticipants and procedureEvery person ≥13 years who registered online with the150 Play List (N = 81,113) was sent an invitation toBerry et al. BMC Public Health         (2018) 18:1300 Page 2 of 11complete the registration questionnaire. The survey wasnot implemented until after the campaign started butparticipants already registered were sent the invitation.The registration survey data were collected from Junethrough November 2017. Participants were asked if theywould be willing to complete another survey in the fu-ture and if so, to provide their first and last names, ande-mail address. Those who did so were sent an invitationto complete the follow-up survey after the campaignended in December 2017. Follow-up data were collectedfrom January 15 to February 25 2018. Surveys were sentin both English and French, based on participant’s pref-erence. Both surveys received ethical approval from auniversity human research ethics board. Informed con-sent was indicated by starting the survey.SurveysThe survey questions are shown in the Additional file 1.The registration survey included questions on demo-graphics, awareness, and 150 Play List participation (i.e.,number of activities from the 150 Play List tried orplanned, and the number of new activities tried, orplanned). The participation questions were developed byParticipACTION for their evaluation and others haveused questions about planned activities as a measure ofongoing intention [11, 12]. Questions about planningand ongoing participation can be considered as forms ofcontinuation intentions [11]. After these items, partici-pants < 18 years were shown Canada’s PA guidelines forchildren and youth (i.e., at least 60 min of moderate tovigorous PA per day). Participants ≥18 years were shownCanada’s PA guidelines for adults (i.e., at least 150 minof moderate to vigorous PA per week). Items regardingPA importance, attitudes, self-efficacy, and intentions(i.e., proximal HOEM constructs) were asked accordingto the age-specific guidelines. Previous research has sup-ported the use of single item measures when they havegood construct validity [13]. The LTPA item has beenvalidated using doubly-labelled water and advocated foruse in population level studies [14]. The follow-up surveyincluded information about 150 Play List participation,importance, attitudes, self-efficacy, intentions, and experi-ence with the 150 Play List. Attitudes toward 150 Play Listadvertisements were also assessed. For that scale, the ‘bor-ing’ and ‘not realistic’ items were reverse-scored, and theinter-rater reliability across all items was good, α = .85,therefore a mean score for attitudes was created.Data analysisNames and email addresses were matched across thetwo surveys and birth year was cross-referenced with thebirthdate the participant provided when registering onthe 150 Play List website. After data were matched, allidentifying information was removed from the data setto comply with ethics. Data were cleaned first by removingcases where two or fewer survey items were responded toand when reported age was < 13 years (the minimum re-sponse age) or when the number of 150 Play List activitiesreported was > 150. Missing data in the remaining surveyswere mean replaced (continuous variables). Missing sexand education were recoded as ‘prefer not to answer’,missing minority group data were recoded as no minoritygroup, and missing LTPA data were recoded as ‘moderate’.Analyses using these data were compared to the results ofanalyses conducted on surveys with no missing data.Alpha was set to .01 for all analyses and Cohen’s d re-ported for between group effect sizes.Key messagesThe responses from the baseline awareness question “Canyou describe any of the key messages?” were first opencoded by one researcher who also wrote code descrip-tions. After initial codes were generated, three researchers(all of whom are bilingual in English and French) dis-cussed them and agreed on the final codes. A randomsample of 1/3 of the responses was selected using SPSS(version 24) and a second researcher coded those usingthe code descriptions to determine inter-rater reliability.Any discrepancies were discussed to assign final codes toresponses. Some responses had up to four different codesembedded within them. For example, the response “getoutside, get moving” received two codes – ‘get active/fit’and ‘get outdoors’. The first rater also created a subcodefor the ‘PA (specific names)’ code. If a specific activity wasmentioned (e.g., walking) then it was given a subcode todetermine how many times a specific activity was men-tioned. Again, multiple subcodes could be given to one re-sponse. For example, the response “Articles aboutwalking, running, weight lifting” received three subcodesto reflect the named activities.Baseline participationA binomial logistic regression was conducted to predictwhether participants reported trying new activities (yes/no).Linear regressions were used to predict the numbers of ac-tivities from the 150 Play List: tried since registration,planned to continue, new activities tried, and new activitiesplanned to continue. In all models, demographic informa-tion (age, sex, education, minority group, LTPA) was en-tered in the first step, whether the 150 Play List or gettingactive in responses to key messages was mentioned (i.e., topof mind awareness) were entered into the second step, andimportance, attitudes, self-efficacy, and intentions in thethird. Using the same variables, a multinomial logistic re-gression was constructed to examine whether participantsplanned ongoing participation (yes/no/not sure).Berry et al. BMC Public Health         (2018) 18:1300 Page 3 of 11Follow-upIntention groups were formed based on the questionasked at baseline and follow-up, “Did any of the activ-ities you tried on the 150 Play List result in on-goingparticipation?” The intention groups formed were suc-cessful intenders (reported ongoing participation at bothassessment points), non-intenders (no ongoing participa-tion at either assessment point), unsuccessful intenders(reported ongoing participation at baseline but not atfollow-up), disinclined actors (reported no ongoing par-ticipation at baseline, but did at follow-up), ambivalentnonactors (were not sure at baseline and reported noongoing participation at follow-up), ambivalent actors(were not sure at baseline but did report ongoing partici-pation at follow-up), and ambivalent (unsure at both as-sessment points) groups were compared to successfulintenders. Repeated measures analysis of variance tests(RM ANOVAs) were used to examine changes over timein number of activities tried since registration, import-ance, attitudes, self-efficacy, and intentions with baselineand follow-up scores as the within subjects variables andintention group as the between subjects variable.Chi-square analyses were calculated to determine differ-ences in intention groups in the impact of the 150 PlayList (e.g., increased PA participation).A binomial logistic regression was conducted to pre-dict whether participants reported trying new activities(yes/no). Logistic regressions were used to predict thenumber of activities from the 150 Play List tried sinceregistration and the number of activities participantsplan to try. In all these models, demographic informa-tion was entered in the first step, whether they men-tioned the 150 Play List or getting active in response tokey messages at baseline were entered into the secondstep, and importance, attitudes, self-efficacy, intentions(all baseline measures), experience with the 150 PlayList, and attitudes toward the 150 Play List advertise-ments were included in step 3. A multinomial logisticregression using the same variables was constructed topredict intention group (non-intenders, unsuccessful in-tenders, disinclined actors, ambivalent nonactors, am-bivalent actors, and ambivalent groups were comparedto successful intenders).ResultsA total of 10,124 people started the baseline registrationsurvey but data from 2248 were removed because theyresponded to two or fewer of the survey items. Data from74 participants were removed because they reported theirage as < 13 years and one reported > 150 activities com-pleted. This left 7801 cases at baseline (n = 7292 English, n= 509 French). Of these, 6824 had no missing data. Missingdata ranged from n = 48 [importance] to n = 184 [inten-tions]). Other missing data included sex (n = 5), education(n = 7), minority group (n = 450), and LTPA (n = 127). Atfollow-up, 1237 English language and 61 French languagesurveys, matched to registration data for within-subjectscomparisons, were completed from a possible 1664.Though no demographic data were available for the un-matched follow-up surveys, no differences existed in thenumber of activities tried or planned, nor in any social cog-nitive variables between the 1298 matched to baseline andthe 366 unmatched follow-up surveys (all p > .09). Table 1shows the demographic characteristics of those who com-pleted only the baseline survey compared to those whocompleted both the baseline and follow-up survey. The dif-ference in time between completing baseline and follow-upsurveys ranged from 35 to 229 days (M = 158.8 [SD =45.33]). No differences existed in time between surveys be-tween any demographic or intention groups (all p > .15),nor was time between surveys correlated with any attitudes,self-efficacy, intentions, or activities tried or planned vari-ables (all r < .09).Key messagesA total of 4201 open-ended responses were provided inEnglish (n = 4012) and French (n = 189). The first codergenerated an initial 14 codes and after discussion 12 codeswere agreed on. Inter-rater reliability was good, kappa = .84,p < .001. Most of the discrepancies were in the ‘150 PlayList/activities’ code. Many of these were simply in the orderthe code was assigned when multiple codes were given toone response. Discrepancies were discussed and final codesassigned. Table 2 shows the final codes, examples of state-ments within these codes, and the number of times eachwas mentioned. Within the ‘PA (specific names)’ code, 81unique activities or sports were mentioned. Those mostoften mentioned were walking (n = 39), running (n = 34),biking (n = 28), yoga (n = 24), and skating (n = 19). Hockeywas the sport most often mentioned in one of its variousforms: ice, ball, street, or sledge (n = 27).Baseline predictors of participationThe results of the regression analyses are shown inTable 3. All models accounted for very small proportionsof variance, but significant correlates of increased par-ticipation included mentioning the 150 Play List or ‘get-ting active’, importance, attitudes, and intentions.Follow-up analysesAll reported analyses were conducted with the originaldata and with missing data replaced. No differences wereobserved in any of the analyses and the results reportedare those with missing data replaced.Differences by intention groupThe seven intention groups included 426 successful in-tenders, 112 non-intenders, 111 unsuccessful intenders,Berry et al. BMC Public Health         (2018) 18:1300 Page 4 of 11Table 1 Demographic information and differences between participants who completed follow-up and those who did notBaseline only (N = 6503) Complete follow-up (N = 1298) Difference testFemale N (%) 5322 (81.8%) 1048 (80.7%) χ2 = 1.63, p = .44Minority N (%) No 5484 (84.3%) 1102 (84.9%) χ2 = 11.68, p = .02More new Canadians and Indigenouspersons did not complete follow-upsurvey than expected.Visible 416 (6.4%) 91 (7.0%)New Canadian 158 (2.4%) 15 (1.2%)Indigenous 140 (2.2%) 20 (1.5%)Disability 305 (4.7%) 70 (5.4%)English N (%) 6055 (93.1%) 1237 (95.3%) χ2 = 8.51, p = .004More English than French completed follow-upEducation N (%) Some high school 153 (2.4%) 14 (1.1%) χ2 = 13.89, p < .02Slightly more educated completed follow-upHigh school 591 (9.1%) 124 (9.6%)Some college 1000 (15.4%) 183 (14.1%)University 3313 (50.9%) 687 (52.9%)Post degree 1325 (20.4%) 275 (21.2%)Prefer not to answer 121 (1.9%) 15 (1.2%)LTPA N (%) Very light 121 (1.9%) 14 (1.1%) χ2 = 7.89, p = .10Light 611 (9.4%) 139 (10.7%)Moderate 2241 (34.5%) 431 (33.2%)Active 2420 (37.2%) 506 (39.0%)Very active 1110 (17.1%) 208 (16.0%)Age M (SD), years 47.84 (13.43) 48.62 (12.58) F (1, 7800) = 3.72, p = .06Importance M (SD) 6.22 (1.08) 6.22 (1.07) F (1, 7800) = 0.038, p = .85Attitudes M (SD) 5.49 (1.57) 5.54 (1.53) F (1, 7800) = 2.78, p = .29Self-efficacy M (SD) 81.77 (21.87) 82.01 (22.03) F (1, 7800) = 0.13, p = .72Intention M (SD) 6.01 (1.30) 5.97 (1.35) F (1, 7800) = 1.54, p = .34Table 2 Final key message codes and the number (%) of baseline and follow-up respondents who gave corresponding responsesCode Name Sample Response BaselineN (%)Follow-upN (%)150 Play List/activities Join the ParticipACTION play list 1133 (14.5) 204 (15.7%)Canada 150 celebration It had to do with Canada’s 150th birthday 328 (4.2) 53 (4.1%)Get active/fit Getting active, getting involved in activitieswith others1841 (23.6) 319 (24.6%)Get outdoors Get outside, play, run, sweat 475 (6.1) 93 (7.2%)Physical activity (specific names) Sledge hockey, street hockey, dancing 657 (8.4) 119 (9.2%)Try something new Try a new sport 308 (3.9) 57 (4.4%)Location of ad specified Global morning news 403 (5.2) 71 (5.5%)Health benefits Good for your health, heart and mind 432 (5.5) 75 (5.8%)Affective response Have fun participating 409 (5.2) 85 (6.5%)PA recommendations Get 150 min of physical activity 51 (0.7) 6 (0.5%)Other advertisements catch phrase The Hal & Joanne Get Fit commercials arewhat comes to mind118 (1.5) 21 (1.6%)Miscellaneous and irrelevant Humans are amazing 263 (6.6) (1.8%)Berry et al. BMC Public Health         (2018) 18:1300 Page 5 of 11Table3Step3F-testsandcoefficientsforthebaselinelinearregressionmodels(numberofactivitiestriedandplantotry),logisticregressionmodel(triednewactivity),andmultinomiallogisticregressionmodel(planongoingparticipation)NumberofactivitiestriedNumberplanningtotryTriednewactivity(comparedtono)Planongoingparticipation(comparedtono)DescriptivesN=7777,M=21.98(SD=28.52)N=7217,M=47.99(SD=48.88)Yesn=4410,Non=3391Yesn=3697,Non=1688,Notsuren=2416F(4,7789)=23.17,p<.001;R2=.037F(4,7789)=43.42,p<.001;R2=.076Χ2=228.62(df=20),p<.001;NagelkerkeR2=.039Χ2=367.95(df=40),p<.001;NagelkerkeR2=.053CategoricalpredictorcategoriesYesYesNotsurePredictorBetaBetaExp(B)Exp(B)Exp(B)Age.013−.201***.991**1.01**1.00Sex.068**.014Prefernottoanswer.8880.4141.172Female.8530.8270.951Malea–––Education.014−.055***Prefernottoanswer.9921.9701.767Highschoolorless1.0961.558**1.121Somecollege1.2111.1991.174College/universitydegree1.0381.0821.031Postdegreea–––Minoritygroup−.024.014Visibleminority1.2511.3761.010NewCanadian1.674*1.820*1.162Indigenous.9581.7951.802Disability1.616**1.4371.374Noa–––LTPA.054**.002Verylightorlight.9251.1861.786**Moderate1.1901.640**2.013**Active1.0121.408**1.526**Veryactivea–––Mentioned150PlayList.044**.074**1.52**1.351**1.151Mentioned‘gettingactive’.060**.053**1.410**1.466**1.357**Importance.064**.058***1.106**1.113**1.005Affectiveattitudes.089**.125***1.072**1.0381.045Self-efficacy−.005−.022.994**.993***.994*Intentions−.006.0331.096***1.305***1.137**a comparisongroup;*p<.01,**p<.001Berry et al. BMC Public Health         (2018) 18:1300 Page 6 of 1193 disinclined actors, 145 ambivalent nonactors, 258 am-bivalent actors, 153 classified as ambivalent. No differ-ences were observed in sex, education, or minority groupby intention group. Successful intenders were significantlyolder (M = 50.73 yrs. [SD = 12.05]) than non-intenders (M= 45.70 yrs. [SD = 13.06]), and unsuccessful intenders (M= 45.12 yrs. [SD = 13.01]); all p < .01. Differences in LTPAwere detected by intention group, χ2 = 54.31, p < .001;non-intender, ambivalent nonactor, and ambivalent groupswere more likely to report very light or light activity; mod-erately active participants were more likely to be ambiva-lent actors; active participants were more likely to reportbeing successful intenders or ambivalent actors; and thevery active were more likely to be disinclined actors orambivalent nonactors. No differences existed by intentiongroups in key message codes mentioned, or reasons forparticipation (e.g., prizes). There were differences in re-ported impact of the Play List, χ2 = 26.19, p < .001.Non-intenders and ambivalent nonactors were less likelyto report engaging in more regular PA or a new activitybecause of the 150 Play List, but they were more likely toreport they were encouraging others to be active, to beless sedentary, or to say it had no impact.For parsimony, only the summary results of the RMANOVAs are reported. A significant increase occurredin overall number of 150 Play List activities tried sinceregistration from baseline (M = 27.11 [SD = 32.30]) tofollow-up (M = 31.88 [SD = 36.44]), but no differencesexisted by intention group. No change was reported inimportance of PA or PA intentions, nor did changeoccur in these constructs by intention group. A changein attitudes was observed by intention group. Post-hoctests showed ambivalent nonactors had a decline in atti-tudes from baseline (M = 5.47 [SD = 1.59]) to follow-up(M = 5.25 [SD = 1.58]), whereas successful intenders hadan increase in attitudes from baseline (M = 5.68 [SD =1.40]) to follow-up M = 5.87 [SD = 1.24]). Finally, a sig-nificant overall decline was observed in self-efficacy frombaseline (M = 82.01 [SD = 22.03]) to follow-up (M =80.72 [SD = 21.89] but no differences existed byintention group. The effects were very small, Cohen’s drange = .01–.14,Significant differences existed in intention groups inthe number of activities planned to continue, the num-ber of new activities tried at follow-up, and the numberof new activities participants plan to continue. In allcases successful intenders reported more activities thanany other intention group, and there were significant dif-ferences in activities successful intenders planned tocontinue (M = 21.78 [SD = 28.08]), and new activitiestried (M = 15.30 [SD = 18.63]), compared tonon-intenders (respective M [SD] = 12.06 [18.50], 9.86[8.34]), or ambivalent nonactors (respective M [SD]=14.28 [23.41], 9.47 [11.06]). Successful intenders alsoreported more new activities tried that they planned tocontinue (M = 8.56 [SD = 13.23]) than ambivalent nonac-tors (M = 4.04 [3.18]). Effect sizes were moderate,Cohen’s d range = .29–.47.Because 203 participants did not report seeing any 150Play List advertisement, the following regression analyseswere conducted with the sample of 1095 who did.Table 4 shows the results of the binomial logistic regres-sion indicating whether participants tried new activitiesat follow-up. Because of some very small cell sizes (i.e.,n < 10), sex, education, and minority groups were not in-cluded in this analysis. There was a positive relationshipbetween trying new activities and citing ‘getting active’in responses to the key message question at baseline,higher rated experience with the 150 Play List, andhigher attitudes toward 150 Play List advertisements.Table 5 shows the results of the multinomial logisticregression predicting intention group with successful in-tenders compared to all other intention groups. Becausethere were no differences in sex, education, or minoritygroups by intention group, and some very small cellsizes (i.e., n < 10), these variables were not included inthis analysis. Non-intenders and ambivalent nonactorshad significantly lower baseline PA intentions than suc-cessful intenders. All intention groups, except ambiva-lent actors, rated their Play List experience as lower thansuccessful intenders. Unsuccessful intenders had signifi-cantly lower attitudes toward the 150 Play List advertise-ments than successful intenders.Table 6 shows the results of the follow-up linear re-gressions. Similar to the baseline findings, very littlevariance was accounted for in any of the models. Higherrated 150 Play List experiences were significantly relatedto all variables. Higher attitudes toward the advertise-ments were significantly related to number of overalland new activities planned to continue.DiscussionMedia and educational campaigns have been identifiedas one population approach to increase PA, yet a 2012scientific statement from the American Heart Associ-ation concluded that there is not enough established evi-dence regarding their efficacy and utility in healthbehavior change [15]. To help address this deficiencythis research longitudinally examined the short-termpost-campaign effects of the 150 Play List on peoplewho registered with the campaign website. The resultsinform whether a year-long campaign like the 150 PlayList can be effective in influencing the behavior of thoseregistered with the campaign.At baseline, about 54% of the sample recalled someform of advertisement that prompted participation in PAor sport; 14.5% of all participants who registered withthe 150 Play List recalled the campaign and 23.6%Berry et al. BMC Public Health         (2018) 18:1300 Page 7 of 11mentioned getting active. Those who named somethingrelated to either of these reported more activities triedand more activities planned to try than those who didnot name them. They were also more likely to say theyhad tried a new activity and planned ongoing participa-tion. This indicates that, similar to others [5], ‘top ofmind’ awareness (i.e., the first thing that comes to mindwhen asked about a particular topic) is likely an import-ant factor to measure, even among people already en-gaged with a mass reach PA campaign. However, atfollow-up, results from the current study indicate thatthese markers of awareness were not related to ongoingTable 4 Tried new activities at follow-up – likelihood of saying yes compared to noStep 1 Step 2 Step 3Χ2 = 6.89 (df = 4), p = .14;Nagelkerke R2 = .009Χ2 = 10.63 (df = 4), p = .005;Nagelkerke R2 = .023Χ2 = 139.82 (df = 6), p < .001;Nagelkerke R2 = .195Predictor Exp (B) Exp (B)Age .99` .991 .984*LTPA Very light orlight0.753 0.735 0.709Moderate 1.060 1.062 1.111Active 1.199 1.180 1.174Very activea – – –Mentioned 150Play List1.340 1.279Mentioned ‘getting active’ 1.622* 1.510Importance 0.966Affective attitudes 1.033Self-efficacy 0.989Intentions 1.113150 Play Listexperience1.643**Ad attitudes 1.796**acomparison group; *p < .01, **p < .001Table 5 Predictors of intention groups – all compared to successful intendersΧ2 = 230.82, p < .001;Nagelkerke R2 = .196Non-intenders UnsuccessfulintendersDisinclined actors AmbivalentnonactorsAmbivalent actors AmbivalentPredictor Exp (B) Exp (B) Exp (B) Exp (B) Exp (B) Exp (B)Age 0.965** .967** .990 .981** .990 .983LTPA Very light or light 1.691 0.740 1.289 1.719 1.606 5.937**Moderate 0.408 1.027 0.512 0.531 1.010 1.258Active 0.489 0.888 0.537 0.507 1.051 1.047Very activea – – – – – –Mentioned 150 Play List 0.594 1.015 1.876 0.884 1.210 0.652Mentioned ‘getting active’ 1.076 1.001 1.036 0.726 1.136 0.655Importance 1.007 1.199 1.594 .973 1.005 1.058Affective attitudes 1.060 1.034 0.808 1.149 0.983 1.064Self-efficacy 1.018 0.991 0.995 1.007 1.003 1.012Intentions 0.608** 0.862 1.165 0.731 0.922 0.819150 Play List experience 0.586** 0.821 0.754* 0.677** .903 0.645**Ad attitudes 0.778 0.479* 0.617 0.660 .628* 0.724acomparison group; * p < .01, **p < .001Berry et al. BMC Public Health         (2018) 18:1300 Page 8 of 11participation in terms of numbers of new activities triedor planned. Only mentioning recall of a ‘getting active’message was positively related to the number of new ac-tivities participants planned to continue. This indicatesthat awareness is likely important at the beginning ofcampaign initiation, but other factors are necessary forongoing participation.Other correlates of participation at baseline were age,with younger adults more likely to plan to try more ac-tivities; but older adults (albeit with extremely smallodds) were more likely to report trying new activities orplanning ongoing participation. Participants with a dis-ability were more likely to have tried a new activity com-pared to those not in a minority group. This mayindicate that the adaptations made for each of the 150activities on the Play List inspired some to try somethingnew or that the 150 Play List was inclusive, irrespectiveof adaptations. Similarly, participants who were new Ca-nadians were also more likely to try a new activity. Al-though Indigenous activities were profiled on the 150Play List, the proportion of participants in the currentresearch who identified as Indigenous (i.e., 2.2%, withonly 1.5% at follow-up) was about half the proportion ofIndigenous persons in the Canadian population (i.e.,4.3% [16]). Further efforts are needed to attract minoritygroups to the website portions of large scale PA cam-paigns. An additional finding is that participants report-ing moderate LTPA or being active compared to thevery active at baseline were more likely to plan ongoingparticipation. Thus, the 150 Play List may have been ef-fective in increasing PA in those who do a little activitybut might not meet Canada’s PA guidelines.In terms of possible outcomes proximal to awareness,perceptions of the importance of PA or attitudes regardingPA did not change and there were small decreases inself-efficacy and intentions, but with very small effect sizes.Other researchers have reported that although self-efficacymay increase at the start of an exercise campaign, it tendsto start to decrease after a few months, a finding the au-thors attribute to the possibility that self-efficacy declines asparticipants realize that long-term adherence is challenging[17, 18]. This may also be demonstrated in the currentstudy in the optimism demonstrated at baseline in whichparticipants planned to try an average of forty-eight 150Play List activities, but at follow-up an average of aboutthirty-two activities had been tried. Though still a substan-tial number of activities, the level of participation (e.g., howlong, at what intensity, how many times) is unknown, andparticipants may have found it difficult to accurately assessself-efficacy at baseline.Table 6 Descriptive statistics, Step 3 F -tests, full model R2 and coefficients for the follow-up linear regression models for number ofactivities from 150 Play List tried and plan to try, and number of NEW activities tried because of the Play List and new activitiesplanned to continueNumber of activitiestried since registrationNumber planningto continuebNumber of NEWactivities triedcNumber of NEWactivities planto continuecDescriptives a N = 1095, M = 32.65(SD = 36.50)N = 1007, M = 17.59(SD = 25.32)N = 807, M = 12.82(SD = 19.14)N = 714, M = 6.47(SD = 13.04)Predictor F (6, 1081) = 21.39,p < .001; R2 = .13F (6, 1081) = 15.42,p < .001; R2 = .09F (6, 793) = 8.22,p < .001; R2 = .07F (6, 700) = 8.76,p < .001; R2 = .09Age −.068 −.081 −.065 −.012Sex .071 .059 .032 .049Education .022 −.029 .017 −.030Minority group .015 −.008 .005 −.054LTPA .027 .009 .080 .060Mentioned 150 Play List .040 .028 .065 .065Mentioned ‘getting active’ .030 −.003 −.013 .094*Importance .035 .019 .038 .101Affective attitudes .022 −.029 −.183** −.043Self-efficacy −.004 .053 .030 −.020Intentions .041 .040 .067 −.046150 Play List experience .307** .212** .228** .198**Ad attitudes .006 .088* −.069 .090* p < .01, **p < .001aOnly participants who had advertisement attitudes data were includedbMissing data in this variable were mean replaced; the results of an analysis with or without mean replacement did not differcbecause of the large amount of missing data and the nature of the question (i.e., participants may not have answered if they didn’t try new activities) theanalyses were conducted without replacing the missing dataBerry et al. BMC Public Health         (2018) 18:1300 Page 9 of 11Further, self-efficacy was not related to participationoutcomes at follow-up. However, participants who re-ported PA as less pleasant reported trying fewer new ac-tivities. Other significant correlates of trying newactivities at follow-up were younger age, more positivereported experience with the 150 Play List, and stron-ger positive attitudes towards the advertisements. Morepositive 150 Play List experience was also related to thenumber of activities tried since registration, the numberof new activities tried, and the number of new activitiesplanned to continue. Thus, and not too surprisingly,ensuring a positive experience with a campaign is es-sential. Taken together, these results highlight the im-portance of a positive affective response to a PAcampaign. There is strong evidence that a positiveaffective response while engaging in moderate PA is re-lated to future PA [19].The intention groups were also informative regardingcampaign impacts and continuation intentions. Partici-pants who indicated that trying a 150 Play List activityresulted in ongoing participation at baseline, and main-tained this at follow-up, were older and reported a morepositive 150 Play List experience. They also had higherPA intentions than non-intenders. The very active weremore likely to be disinclined actors or ambivalent nonac-tors. Though counter to prior research [10], this findingmay be due to the 150 Play List specificity in our meas-urement of intentions. Specifically, the very active arelikely already involved in a lot of activities and, althoughinitially attracted to the campaign, they may not havecontinued because they were already engaged in otheractivities. Non-intender, ambivalent nonactor, and am-bivalent groups were more likely to report very light orlight activity and moderately active participants weremore likely to be ambivalent actors. Non-intender, am-bivalent nonactor, and ambivalent groups were morelikely to report very light or light activity and moderatelyactive participants were more likely to be ambivalent ac-tors. This may indicate that motivation among theambivalent and ambivalent nonactor groups was lack-ing because they were not sure if they would con-tinue to participate in 150 Play List activities. Asargued by Rhodes and Rebar [20], intention can beconsidered as the magnitude of strength of convictionto achieve a goal. However, it is encouraging thatmoderately active participants were more likely tochange from being not sure about ongoing participa-tion to indicating that the activities they tried did re-sult in ongoing participation. Thus, as already noted,the 150 Play List may have been effective in moti-vating somewhat active people to be more active.However, those who did not intend further parti-cipation, or remained ambivalent, were not very activecommensurate with intention-based theories [21].Non-intenders and ambivalent nonactors were morelikely to report they were encouraging others to beactive or taking steps to being less sedentary. Thus,even though the campaign may not have affectedtheir PA, it may have affected their intentions to helpothers be active, or become motivated to reduce sed-entary activity. These are important actions, inde-pendent of PA behavior.The strengths of this research include the longitu-dinal follow-up (with repeated measures) of a rela-tively large sample size and the application oftheoretical frameworks in guiding analysis and inter-pretation. However, several limitations should bementioned. First, the majority of the sample werewomen. These results are similar to participants fromthe web-based “Canada on the Move” campaign thatused cereal boxes to distribute ‘free’ pedometers andasked people to visit a website to ‘donate their stepsto science’ [22]. Why women are more attracted toregistering for web-based campaigns warrants furtherconsideration. A second limitation is that the samplethat provided survey responses are likely biased to-ward the 150 Play List or to PA in general. However,even among this sample, important correlates andpatterns of behavior were identified. Further, althoughprevious research has supported the use of singleitem measures [13] using single items could reducereliability. The questions regarding ongoing participa-tion were used to create the intention groups. It ispossible they measure behavioral stability rather thanintention. However, the response option ‘not sure’,which about 43% of participants chose at one or bothtime points, likely indicates participants’ questioningwhether they will continue the behavior, and thusmay be related to intentions. A final limitation is thatsome participants completed the survey after they hadbeen in the campaign for several months.ConclusionIn conclusion, this research helps address the lack ofevidence regarding the efficacy of mass reach PA cam-paigns. Specifically, this research reinforces the ideathat ‘top of mind’ awareness should be measured. Posi-tively, results showed that even those who report verylight activity may still engage in such a campaign tohelp others or to reduce sedentary behaviors. The adap-tations of each activity described on the website mayhave encouraged people with disabilities to try new ac-tivities, but it is still not clear how to attract minoritygroups to engage with the website portion of such cam-paigns. This research identifies important characteris-tics of who may be attracted to such a campaign andwho may wish to stay engaged.Berry et al. BMC Public Health         (2018) 18:1300 Page 10 of 11Additional fileAdditional file 1: Survey questions. A table containing all items fromthe baseline and follow-up surveys by question category: demographics,awareness, 150 Play List participation, Proximal HOEM constructs, Leisure-time PA, Attitudes toward advertisements. (DOCX 19 kb)AbbreviationsHOEM : Hierarchy of Effects Model; PA: physical activityAcknowledgementsWe would like to thank Tala Chulak-Bozzer from ParticipACTION for her helpin developing and administering the surveys.FundingTRB and ALC are supported by the Canada Research Chairs Program. GF issupported by a Canadian Institutes of Health Research-Public Health Agencyof Canada Chair in Applied Public Health. LMV holds a Canadian Institutes ofHealth Research Fellowship Award. None of the funding bodies had any rolein any part of the research, including writing or reviewing the manuscript.Availability of data and materialsThe datasets used and/or analysed during the current study are available onthe University of Alberta’s dataverse, access to can be requested from thecorresponding author.Authors’ contributionsTRB led the data analysis and wrote the first draft of the manuscript. EMOled the coding of the key messages. KF aided in data analysis. RR providedtheoretical input. TRB, RR, GF, ALC, NR, JS, MT, and LV provided input intosurvey development and data acquisition. All authors were involved inwriting and critically revising the manuscript. All authors have read andapproved the manuscript.Competing interestSeveral authors on this manuscript are members of ParticipACTION’sResearch Advisory Group. LMV is the Knowledge Translation Manager atParticipACTION. The surveys were administered by ParticipACTION as part oftheir campaign evaluation, but ParticipACTION was not involved in the dataanalysis or writing of the present paper.Ethics approval and consent to participateEthics approval was obtained from the University of Alberta Health ResearchEthics Board (applications Pro00072571 and Pro00078545). Information onparticipation was provided online prior to starting the surveys and consentgiven by starting the surveys. The wording in the information section at thestart of the survey, after information to provide informed consent regardingconfidentiality, how the data will be used, etc.… was “By doing the survey,you are saying that you read and understood the information above. Youunderstand that completing the survey is your choice and you can stop atany time or not answer any questions. I agree to do the survey”Consent for publicationNot applicablePublisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Faculty of Kinesiology, Sport, and Recreation, 1-153 University Hall;University of Alberta, T6G 2H9, Edmonton, AB 780 492 3280, Canada.2University of Victoria, Victoria, Canada. 3University of British Columbia, BritishColumbia, Canada. 4Queen’s University, Kingston, Canada. 5University ofGuelph, Guelph, Canada. 6Children’s Hospital of Eastern Ontario ResearchInstitute, Ontario, Canada. 7ParticipACTION, Ontario, Canada.Received: 11 June 2018 Accepted: 14 November 2018References1. Bauman A, Chau J. The role of media in promoting physical activity. J PhysAct Health. 2009;6(Suppl 2):S196–210.2. Yun L, Ori E, Lee Y, Berry TR, Sivak AA. Systematic review of mass mediacampaigns to promote physical activity: an update from 2010. J Phys ActHealth. 2017;14:552–70.3. ParticipACTION. ParticipACTION 150 Play List. https://www.participaction.com/en-ca/programs/participaction-150-play-list. Accessed 3 May 2018.4. Cavill N, Bauman A. Changing the way people think about health-enhancing physical activity: do mass media campaigns have a role? J SportsSci. 2004;22(8):771–90.5. Yun L, Berry TR. Examining implicit cognitions in the evaluation of acommunity-wide physical activity program. Eval Program Plan. 2018;69:10–7.6. Spence JC, Brawley LR, Craig CL, Plotnikoff RC, Tremblay MS, Bauman A,et al. ParticipACTION: awareness of the ParticipACTION campaign amongCanadian adults - examining the knowledge gap hypothesis and ahierarchy-of-effects model. Int J Behav Nutr Phys Act. 2011;6:85.7. Craig CL, Bauman A, RegerNash B. Testing the hierarchy of effects model:ParticipACTION's serial mass communication campaigns on physical activityin Canada. Health Promot Internation. 2010;25(1):14–23.8. Bauman A, Bowles HR, Huhman M, Heitzler CD, Owen N, Smith BJ, et al.Testing a hierarchy of effects model: pathways from awareness tooutcomes in the VERB campaign 2002-2003. Am J Prev Med. 2008;34(6,Suppl 1):S249–56.9. Rhodes RE, de Bruijn GJ. How big is the physical activity intention-behaviourgap? A meta-analysis using the action control framework. Brit J HealthPsych. 2013;18:296–309.10. Rhodes RE, de Bruijn GJ. What predicts intention-behavior discordance? Areview of the action control framework. Exerc Sport Sci Re 2013;41:, 201–207.11. Chatzisarantis NLD, Hagger M. Influences of personality traits andcontinuation intentions on physical activity participation within the theoryof planned behaviour. Psychol Health. 2008;23:347–67.12. Courneya KS, Bobick TM, Schinke RJ. Does the theory of planned behaviormediate the relation between personality and exercise behaviour? BasicAppl Soc Psych. 1999;21:317–24.13. Gardner D, Cummings L, Dunham R, Pierce J. Single-item versus multiple-item measurement scales: an empirical comparison. Educ Psychol Meas.1998;58:898–915.14. Johansson G, Westerterp KR. Assessment of the physical activity level withtwo questions: validation with doubly labeled water. Int J Obes. 2008;32:1031–3. https://doi.org/10.1038/ijo.2008.42.15. Mozaffarian D, Afshin A, Benowitz NL, et al. Population approaches toimprove diet, physical activity, and smoking habits: a scientific statementfrom the American Heart Association. Circulation. 2012;126(12):1514–63.16. Statistics Canada. Aboriginal peoples in Canada: first nations people, Métisand Inuit. 2011. https://www12.statcan.gc.ca/nhs-enm/2011/as-sa/99-011-x/99-011-x2011001-eng.cfm. Accessed 22 May 2018.17. Rodgers WM, Murray TC, Courneya KS, Bell GJ, Harber VJ. The specificity ofself-efficacy over the course of a progressive exercise program. Appl Psych:Health Well-being. 2009;1:211–32.18. Spence JC, Burgess JA, Rodgers W, Murray T. Effect of pretesting onintentions and behaviour: a pedometer and walking intervention. PsycholHealth. 2009;24:777–89.19. Rhodes RE, Kates A. Can the affective response to exercise predict futuremotives and physical activity behavior? A systematic review of publishedevidence. Ann Behav Med. 2015;49(5):715–31. https://doi.org/10.1007/s12160-015-9704-5.20. Rhodes RE, Rebar A. Conceptualizing and defining the intention constructfor future physical activity research. Exerc Sport Sci Rev. 2017;45:209–16.21. Fishbein M, Triandis HC, Kanfer FH, Becker M, Middlestadt SE, Eichler A.Factors influencing behavior and behavior change. In: Baum A, RevensonTA, editors. Handbook of health psychology. Mahwah. New Jersey:Lawrence Erlbaum Associates; 2001. p. 3–17.22. Plotnikoff RC, Spence JC, Tavares LS, Rovniak LS, Bauman A, Lear SA,McCargar L. Characteristics of participants visiting the Canada on the movewebsite. Can J Public Health 2006;97 Suppl 1:S28–S35, S30–8.Berry et al. 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