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Physical activity screening to recruit inactive randomized controlled trial participants: how much is… Vandelanotte, Corneel; Stanton, Robert; Rebar, Amanda L; Van Itallie, Anetta K; Caperchione, Cristina M; Duncan, Mitch J; Savage, Trevor N; Rosenkranz, Richard R; Kolt, Gregory S Oct 8, 2015

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LETTER Open AccessPhysical activity screening to recruitinactive randomized controlled trialparticipants: how much is too much?Corneel Vandelanotte1*, Robert Stanton2, Amanda L. Rebar1, Anetta K. Van Itallie1, Cristina M. Caperchione3,Mitch J. Duncan4, Trevor N. Savage5, Richard R. Rosenkranz6 and Gregory S. Kolt5AbstractScreening physical activity levels is common in trials to increase physical activity in inactive populations. Commonlyapplied single-item screening tools might not always be effective in identifying those who are inactive. We appliedthe more extensive Active Australia Survey to identify inactive people among those who had initially beenmisclassified as too active using a single-item measure. Those enrolled after the Active Australia Survey screeninghad significantly higher physical activity levels at subsequent baseline assessment. Thus, more extensive screeningmeasures might result in the inclusion of participants who would otherwise be excluded, possibly introducingunwanted bias.Trial registration: Australian New Zealand Clinical Trials Registry, ACTRN12611000157976.Keywords: measurement, physical activity, randomized controlled trial, recruitment, screening, single-item, studydesign, study protocolFindingsLong-term physical inactivity is associated with increasedlevels of chronic disease and reduced quality of life [1].The majority of the population, however, does not engagein enough physical activity to gain optimum healthbenefits [2]. Randomized controlled trials are beingconducted, to examine the effectiveness of interven-tions to increase physical activity. Many of these trialsscreen physical activity levels of potential study partici-pants with the aim of only recruiting participants who arelow-active [3].Pre-randomization screening to enroll low-active par-ticipants might increase intervention effect sizes; whichmight necessitate a smaller study sample to demonstratebetween-group differences [3]. People already meetingphysical activity recommendations are often attracted tophysical activity interventions, though they are typicallynot the target audience for these kinds of intervention,as there are fewer public health benefits in increasingactivity in active people, especially when resources arelimited [4]. Participants with low activity levels also havea greater capacity to increase activity levels and ceilingeffects are less likely to occur; thus, the likelihood thatthe intervention will be successful in demonstrating itseffectiveness is greater [5]. Finally, active people areknown to be responsive to physical activity messages,even in the absence of an intervention to help thembecome more active. Therefore, to decrease the probabil-ity of the control group becoming more active (whichwould undermine study outcomes) recruiting a samplethat is resistant to change in the absence of an interven-tion (i.e., inactive people) is beneficial [6].Waters et al. [3] indicated that 18 out of 23 trials thatthey evaluated implemented some sort of physical activityscreening, but that the methods applied and the physicalactivity cut-off points used to determine eligibility variedgreatly. The majority of studies have used single-itemphysical activity screening tools, mainly to ensure thatparticipant recruitment is efficient and of little burdento those wanting to participate [7]. Although some stud-ies have demonstrated acceptable validity for single-itemmeasures [7], it has also been commonly reported that* Correspondence: c.vandelanotte@cqu.edu.au1Physical Activity Research Group, School for Human Health and SocialScience, Central Queensland University, Rockhampton, QLD, AustraliaFull list of author information is available at the end of the articleTRIALS© 2015 Vandelanotte et al. 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.Vandelanotte et al. Trials  (2015) 16:446 DOI 10.1186/s13063-015-0976-7large proportions of participants still meet physical activ-ity guidelines at the trial baseline assessment when morerigorous physical activity measures are applied, such asfull-length questionnaires or objective accelerometer-based measures [8].In this study, we examined the effectiveness of a two-stage physical activity screening process as part of athree-arm randomized controlled trial (‘Walk 2.0’) [9].The Walk 2.0 trial investigated the effectiveness of twoweb-based physical activity interventions (a traditionalweb 1.0 intervention and an intervention including on-line social networking and other web 2.0 components)compared with a print-based intervention [9]. A total of1244 people completed the initial screening question-naire and 504 people were randomized into one of threegroups. People were excluded based on a number ofscreening criteria (e.g., under 18 years of age, no internetaccess, medical condition that prevents increased activ-ity), although the numbers presented here only relate tothe outcomes of the physical activity screening. A fulloverview of why and at what stage participants wereexcluded from the trial will be presented elsewhere.Physical activity screening was applied in two steps.The first step included application of a commonly usedsingle-item physical activity measure: ‘As a rule, do youengage in at least half an hour of moderate or vigorousexercise (such as walking or a sport) on five or moredays of the week (yes/no)?’ [10]. This measure hasdemonstrated good concurrent validity, identifying 77 %of those who were physically inactive according to a moreextensive assessment of physical activity [11]. Those whoanswered ‘no’ to this single-item measure were deemedeligible to participate; however, those who answered‘yes’ underwent a second step of telephone-administeredscreening, using the Active Australia Survey [12]. Thissurvey includes items to assess the duration and frequencyof walking, and of moderate and vigorous physical activityin the previous week and has demonstrated acceptablereliability and validity [13]. Participants were only eligibleto participate when reporting less than 150 min/week ofphysical activity using this survey. This two-step protocolwas applied to minimize participant burden (for thosewho were eligible after the first step), and maximize theuse of resources allocated to recruitment (to ensure thateligible participants were not excluded if the initial assess-ment was inaccurate).A total of 418 people answered ‘no’ to the single-itemmeasure and were accepted into the trial, whereas the 370people who answered ‘yes’ underwent further screeningusing the Active Australia Survey. Of these, 284 (76.8 %)reported more than 150 min/week of physical activity andwere excluded as too active to participate in the trial. Theaverage level of physical activity of those excluded was306 ± 217 min/week. The 86 (23.2 %) people who wereincluded reported an average of 80 ± 48 min/week ofphysical activity at the time of screening.When examining baseline physical activity levels (seeTable 1), we observed not only that a large number ofparticipants were exceeding 150 min/week of physicalactivity regardless of screening protocol, but also thatthose who had participated in the two-step screeningprocess were significantly more physically active at base-line assessment (measured both subjectively and object-ively (Actigraph accelerometry)), compared with thosewho had only undergone the single-item screening.While some test values only trended toward significance(P = 0.05–0.10), the differences between groups seem tobe mainly driven by differences in walking levels.An important point to note is that neither screeningapproach (single-step or dual-step) was particularly effect-ive in preventing people who were too active from beingenrolled in the study; and that screening using objectiveTable 1 Baseline physical activity for participants screened with a single-item only (first step) and for those screened moreextensively (second step)Baseline physical activity outcomes First step screening, (n = 418) Second step screening (n = 86) t or χ2 test PActive Australia Survey:Walking (min/week) 119 ± 149 162 ± 168 2.37 0.018Moderate intensity (min/week) 38 ± 97 27 ± 63 −1.02 0.307Vigorous intensity (min/week) 46 ± 95 44 ± 85 −0.15 0.881Walking +moderate + vigorous intensity (min/week) 204 ± 247 234 ± 216 1.04 0.297Achieves 150 min/week of moderate + vigorous intensity (%) 41.2 55.8 6.14 0.013Actigraph Accelerometry:Steps (number/day) 7057 ± 2314 8166 ± 2727 3.41 0.001Moderate intensity (min/week) 157 ± 118 190 ± 140 2.61 0.031Vigorous intensity (min/week) 4.9 ± 21.3 2.3 ± 7.3 −1.89 0.059Moderate + vigorous intensity (min/week) 162 ± 141 192 ± 141 1.91 0.057Achieves 150 min/week of moderate + vigorous intensity (%) 43.6 50.6 1.33 0.249Vandelanotte et al. Trials  (2015) 16:446 Page 2 of 3measures (accelerometry)—despite being burdensome,time intensively, and costly—may thus be preferable, asfewer active participants would subsequently be enrolled.Further, it is challenging to explain the counterintuitivedifferences with regards of single- and dual-step screeningoutcomes. One plausible explanation may be related tosocial desirability bias [14]. That is, some participants mayhave sensed their likelihood of inclusion in the random-ized controlled trial would be higher if reporting a lowerlevel of physical activity on the subsequent screeningmeasure. Another explanation may be that the moreextensive screening increased physical activity awareness[3], resulting in more efforts to be active prior to the base-line assessment. These findings highlight that recruitmentprocedures need to be designed carefully, as they mightintroduce potentially unwanted and unexpected biases.Competing interestsThe authors declare that they have no competing interests.Authors’ contributionsCV, MJD, CMC, and GSK, conceived the project and procured the projectfunding. GSK led the coordination of the trial. GSK, RRR, CV, MJD, and CMCassisted with the protocol design. TNS is managing the trial, including datacollection with the contributions from the data manager AKVI. CV, RS, TNS,and ALR interpreted the data and drafted the manuscript. All authors read,edited, and approved the final manuscript.AcknowledgementsThis trial was funded by the National Health and Medical Research Council(Project Grant number 589903). The funder did not have any role in thestudy other than to provide funding. MJD (ID 100029) and CV (ID 100427)are supported by a Future Leader Fellowship from the National HeartFoundation of Australia.Author details1Physical Activity Research Group, School for Human Health and SocialScience, Central Queensland University, Rockhampton, QLD, Australia.2School for Medical and Applied Sciences, Central Queensland University,Rockhampton, QLD, Australia. 3School of Health and Exercise Sciences,University of British Columbia, Kelowna, BC, Canada. 4Priority Research Centrefor Physical Activity and Nutrition, Faculty of Health and Medicine, School ofMedicine and Public Health, University of Newcastle, Newcastle, NSW,Australia. 5School of Science and Health, Western Sydney University, Sydney,NSW, Australia. 6Department of Human Nutrition, Kansas State University,Manhattan, KS, USA.Received: 17 May 2015 Accepted: 25 September 2015References1. Warburton DER, Nical CW, Bredin SSD. Health benefits of physical activity:the evidence. Can Med Assoc J. 2006;174:801–9.2. Sisson SB, Katzmarzyk PT. International prevalence of physical activity inyouth and adults. Obesity Rev. 2008;9:606–14.3. Waters LA, Winkler EA, Reeves MM, Fjeldsoe BS, Eakin EG. The impact ofbehavioural screening on intervention outcomes in a randomisedcontrolled multiple behaviour intervention trial. IJBNPA. 2011;8:24.4. Prochaska JJ, Sallis JF, Long B. A physical activity screening measure for usewith adolescents in primary care. Arch Ped Adolesc Med. 2001;155:554–9.5. Snyder CD, Sloane R, Haines PS, Mille P, Clipp EC, Morey MC, et al. The DietQuality Index-revised: a tool to promote and evaluate dietary changeamong older cancer survivors enrolled in a home-based intervention trial.J Am Diet Assoc. 2007;107:1519–29.6. van Stralen MM, Lecher L, Mudde AN, de Vries H, Bolman C. Determinantsof awareness, initiation and maintenance of physical activity among theover fifties: a Delphi study. Health Edu Res. 2010;25:233–47.7. Milton K, Bull FC, Bauman A. Reliability and validity testing of a single-itemphysical activity measure. Brit J Sport Med. 2011;45:203–8.8. Jennings CA, Vandelanotte C, Caperchione CM, Mummer WK. Effectivenessof a web-based physical activity intervention for adults with Type 2diabetes—a randomised controlled trial. Prev Med. 2014;60:33–40.9. Kolt GS, Rosenkranz RR, Savages TN, Maeder AJ, Vandelanotte C, Duncan MJ,et al. Walk 2.0—using Web 2.0 applications to promote health-relatedphysical activity: a randomised controlled trial protocol. BMC Pub Health.2013;13:436.10. Elley C, Kerse N, Arroll B, Robinson E. Effectiveness of counselling patientson physical activity in general practice: cluster randomised controlled trial.Brit Med J. 2003;362:793–8.11. Rose SB, Elley CR, Lawton BA, Dowell AC. A single question reliably identifiesphysical inactive women in primary care. NZ Med J. 2008;121:40–6.12. Australian Institute of Health and Welfare. The Active Australia Survey; aguide and manual for implementation analysis and reporting. Canberra:Australian Institute of Health and Welfare; 2003.13. Brown W, Bauman A, Chey T, Trost S, Mummery K. Comparison of surveysused to measure physical activity. Aust NZ J Public Health. 2004;28:128–34.14. Brenner PS, DeLamater JD. Social desirability bias in self-reports of physicalactivity: is an exercise identity the culprit? Soc Indic Res. 2014;117(2):489–504.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitVandelanotte et al. Trials  (2015) 16:446 Page 3 of 3


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