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The effectiveness of a web 2.0 physical activity intervention in older adults – a randomised controlled… Alley, Stephanie J; Kolt, Gregory S; Duncan, Mitch J; Caperchione, Cristina M; Savage, Trevor N; Maeder, Anthony J; Rosenkranz, Richard R; Tague, Rhys; Van Itallie, Anetta K; Kerry Mummery, W.; Vandelanotte, Corneel Jan 12, 2018

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RESEARCH Open AccessThe effectiveness of a web 2.0 physicalactivity intervention in older adults –a randomised controlled trialStephanie J. Alley1* , Gregory S. Kolt2, Mitch J. Duncan3, Cristina M. Caperchione4, Trevor N. Savage5,Anthony J. Maeder6, Richard R. Rosenkranz7, Rhys Tague8, Anetta K. Van Itallie1, W. Kerry Mummery9and Corneel Vandelanotte1AbstractBackground: Interactive web-based physical activity interventions using Web 2.0 features (e.g., social networking)have the potential to improve engagement and effectiveness compared to static Web 1.0 interventions. However,older adults may engage with Web 2.0 interventions differently than younger adults. The aims of this study were todetermine whether an interaction between intervention (Web 2.0 and Web 1.0) and age group (<55y and ≥55y)exists for website usage and to determine whether an interaction between intervention (Web 2.0, Web 1.0 andlogbook) and age group (<55y and ≥55y) exists for intervention effectiveness (changes in physical activity).Methods: As part of the WALK 2.0 trial, 504 Australian adults were randomly assigned to receive either a paperlogbook (n = 171), a Web 1.0 (n = 165) or a Web 2.0 (n = 168) physical activity intervention. Moderate to vigorousphysical activity was measured using ActiGraph monitors at baseline 3, 12 and 18 months. Website usage statisticsincluding time on site, number of log-ins and number of step entries were also recorded. Generalised linear andintention-to-treat linear mixed models were used to test interactions between intervention and age groups (<55yand ≥55y) for website usage and moderate to vigorous physical activity changes.Results: Time on site was higher for the Web 2.0 compared to the Web 1.0 intervention from baseline to3 months, and this difference was significantly greater in the older group (OR = 1.47, 95%CI = 1.01–2.14, p = .047).Participants in the Web 2.0 group increased their activity more than the logbook group at 3 months, and thisdifference was significantly greater in the older group (moderate to vigorous physical activity adjusted meandifference = 13.74, 95%CI = 1.08–26.40 min per day, p = .03). No intervention by age interactions were observed forWeb 1.0 and logbook groups.Conclusions: Results partially support the use of Web 2.0 features to improve adults over 55 s’ engagement in andbehaviour changes from web-based physical activity interventions.Trial registration: ACTRN ACTRN12611000157976, Registered 7 March 2011.Keywords: Physical activity, Intervention, Internet, Online, Web 2.0, Older adults* Correspondence: s.alley@cqu.edu.au1Physical Activity Research Group, Appleton Institute, School of Health,Medical and Applied Sciences, Central Queensland University, Rockhampton,QLD 4702, AustraliaFull 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.Alley et al. International Journal of Behavioral Nutrition and Physical Activity (2018) 15:4 DOI 10.1186/s12966-017-0641-5BackgroundPhysical activity improves physical and mental health,reduces the risk of chronic disease and improves gen-eral health and wellbeing [1]. It is estimated that indi-viduals who are physically active have a 30% to 50%lower risk of chronic disease [2–4]. Physical activity isparticularly important for older adults as chronic dis-ease risk increases with age [5]. Physical activity alsoreduces the risk of falls by 17% in older adults [6]and improves symptoms in those diagnosed with de-pression or dementia [7, 8] which are more commonin older adults [5, 9]. Despite the health benefits ofphysical activity, only 48% of Australian adults aremeeting the physical activity guidelines for goodhealth, and this is even lower in adults aged 55–65(43%) and 65–75 (40%) [10]. Inactivity is contributingto the burden of Australia’s aging population on thehealth care system [11, 12]. Interventions are neededto promote physical activity in older adults to helpthem to maintain their health and prevent chronicdisease and mental health problems as they age [13].The Internet is an effective way to deliver physicalactivity interventions in adult populations [14–16].Web-based interventions have time, geographical andfinancial advantages over face-to-face interventions.This enables programs to be delivered to largepopulations at low cost [17, 18] and they have alsodemonstrated that they can be as effective as face-to-face interventions [19]. Older adults are the fastestgrowing age group of Internet users, with 77% ofAustralian adults aged 55+ years already connected[20]. Web-based physical activity interventions arewell accepted by older adults and older participantshave been found to have greater increases in physicalactivity compared to younger participants [21]. Assuch, web-based interventions are potentially wellsuited to older adults, but this area is under-researched.Challenges with web-based physical activity interven-tions in adults of all ages include low satisfaction, web-site usage and retention. This limits long-termbehavioural outcomes, as higher intervention exposureis associated with more positive behavioural outcomes[22, 23]. Greater website interactivity has shown to im-prove website usage and engagement in middle ageadults [24]. This may also be the case in older adults,however their Internet literacy remains lower, whichmay influence how they use more complex and inter-active websites [25]. Therefore, it is not known if en-hanced interactivity in a web-based physical activityintervention is effective at engaging older adults in termsof improved satisfaction, usability and website usage andif satisfaction, usability and website usage is higher inolder adults with higher Internet literacy [26, 27].Next generation Web 2.0 applications have potentialto improve the interactivity and engagement of staticWeb 1.0 health websites with fixed content. Web 2.0applications are aimed at giving users control of howinformation is generated and shared, and include socialnetworking, blogs, wikis, podcasts, mash-ups and videosharing sites. Web 2.0 has become common on theInternet, and users have become accustomed to thislevel of interactivity [28, 29]. In response to the high useof Web 2.0 applications, some recent web-based physicalactivity interventions have included Web 2.0 applications[30]. Maher, Lewis [30] found in their systematic reviewon web-based health behaviour change interventions in-corporating online social networks that 9 out of 10 inter-ventions lead to positive health behaviour changes.Social networking use is higher and more frequent inyounger adults, with 79% of adults 30–49 years beingFacebook users in 2015 [29]. However, there has been anincrease of older adults using such applications. Forexample, the percentage of older adults aged 50–65 yearsand 65+ years with Internet access who used the socialnetworking site, Facebook rose between 2012 to 2015(52 to 64% and 35 to 48% respectively) [31]. Despite therise in the number of older adults using Web 2.0 appli-cations, older adults’ lower and less frequent use of Web2.0 features may mean that they have a lower satisfactionand usage of Web 2.0 features in a web-based physicalactivity intervention when compared to younger adults.This study builds on a previous RCT that demonstratedgreater website usage and physical activity improvementsof a Web 2.0 website for increasing physical activity com-pared to a Web 1.0 website in a sample with high age vari-ability [32]. The first aim of this study was to determinewhether an interaction between intervention (Web 2.0and Web 1.0) and age group (<55y and ≥55y) exists forintervention satisfaction, usability and website usage(assessed through non-usage attrition, website visits, timeon site, days with step entry). The second aim of this studywas to determine whether an interaction between inter-vention (Web 2.0, Web 1.0 and logbook) and age group(<55y and ≥55y) exists for intervention effectiveness(changes in moderate to vigorous physical activity andstep counts). The third aim was to investigate whether aninteraction between Internet self-efficacy and interventiongroup (Web 2.0, Web 1.0 and logbook) exists for satisfac-tion, usability, website usage, and intervention effective-ness in older adults.MethodsTrial designThis paper used data from the WALK 2.0 study [32], athree-arm randomised controlled trial investigating theefficacy of a web-based physical activity interventionwith Web 2.0 features in comparison to a Web 1.0Alley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 2 of 11physical activity intervention and a physical activitylogbook. Outcomes were assessed at baseline and at3, 12, and 18 months. A detailed description of thetrial protocol can be found elsewhere [33]. The find-ings demonstrated that the Web 2.0 group had higherlevels of website engagement and physical activitychanges compared to the Web 1.0 group at 3 monthsbut not at 12 and 18 months [32]. The data wascollected according to CONSORT guidelines (seeAdditional file 1 for CONSORT checklist).Recruitment and participantsRecruitment methods for the WALK 2.0 study have beendescribed in detail elsewhere [34]. In summary, a total of15,526 Australians were invited to participate in the trial.Recruitment was undertaken through personalised lettersto 7000 individuals in Capricornia (Central Rockhampton,QLD) and 7000 individuals in Werriwa (South WesternSydney, NSW) whose addresses were obtained from theAustralian Electoral Commission database. Emails fromthe Population Research Laboratory to past research par-ticipants who indicated they would be interested in par-ticipating in future research and emails delivered throughUniversity email lists were also implemented.An eligibility survey was sent along with the recruit-ment letters for potential participants to complete if theywere interested in participating. An online version of thesurvey was also available. Participants were eligible toparticipate if they 1) lived or worked in Rockhampton orSouth Western Sydney, 2) were interested in increasingtheir physical activity, and 3) were over 18 years of age.Participants were excluded from the project if 1) theydid not have access to the Internet, 2) were unable tospeak/read English, 3) were engaging in moderate-to-vigorous PA (MVPA) for 30 min on 5 or more days perweek, assessed with the question “as a rule, do you do atleast half an hour of moderate or vigorous exercise (suchas walking or a sport) on five or more days a week?” 4)had an existing chronic medical condition potentiallymaking them at risk of injury or ill health from increas-ing their physical activity (assessed using the PhysicalActivity Readiness Questionnaire, PAR-Q) and 5) hadpreviously participated in the 10,000 Steps program(www.10000steps.org.au) (see Additional file 2 for moreinfomation on sample size, participant recruitment andhow missing data were handled).ProcedureEligible participants were invited to attend an induc-tion session to receive detailed information aboutthe study and provide informed consent. Baselinephysical activity data was then collected through anActiGraph activity monitor for a week before partici-pants attended their baseline measurement session.All measurement sessions (baseline, 3, 12 and18 months) were conducted face-to-face to collectanthropometic measures and self-report question-naire responses. ActiGraph monitors were posted toparticipants a week before they attended their 3, 12and 18 month measurement sessions. After baselinemeasures were collected participants were given apedometer to track their steps and were randomlyallocated to one of the three trial arms using equalgroups random allocation performed through acomputer-generated algorithm (Fig. 1). A projectmanager enrolled and assigned participants to groupsduring March 2012–June 2013.InterventionsWeb 1.0Participants in the Web 1.0 group gained access to theexisting 10,000 Steps website (www.10000steps.org.au).The 10,000 Steps Australia project is a community basedphysical activity project which has been running since2001 and is funded by a state-wide health authority inQueensland, Australia. In conjunction with the use of apedometer, the project specific website allows partici-pants to keep track of the number of steps they takeevery day, set goals and participate in challenges. Thewebsite uses standard Web 1.0 features such as dataentry and text forum submissions, based on the users’individual, static interactions with the site. Educationalmaterials are also available on the website. Inter-participant communication is limited to a public forumand data feed from a virtual walking buddy feature,which enables a user to share their step log with anotheruser. Users must know the email address of their walk-ing buddy to connect.Web 2.0Participants in the Web 2.0 group gained access to anewly developed website (www.walk.org.au). TheWALK 2.0 website was developed to add to the10,000 Steps website functionality with the aim tocreate a more interactive environment containingadditional Web 2.0 features to increase opportunitiesfor contact between participants. The Web 2.0 fea-tures included ‘status updates’, streams, blogs, in-ternal emails, and forum posts. Participants had apersonalised home page, allowing them to access spe-cific information about their progress, choose fea-tures such as mapping their favourite walks using aGoogle ‘mashup’ tool, opportunistically ‘befriend’other users, view their ‘friend’s’ updates, make com-ments and invite friends and family not part of theintervention study to join the site. Users also had apersonal profile page, which allowed them to sharepersonalised updates with their ‘friends’ on the site.Alley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 3 of 11LogbookParticipants in the control condition were instructednot to register or use the publicly available 10,000Steps website and received access to a paper-basedlogbook. The logbook allowed participants to recordtheir steps and monitor their progress and providedparticipants with hard copy educational materialswhich were available on the intervention websites(see Additional file 3 for TIDieR checklist of theinterventions).MeasuresDemographicsParticipant demographics were collected including age,gender (male, female), education (higher education, trade/diploma, high school), occupation (white collar, bluecollar, professional, other), income (<$1000, $1000–$1999,$2000–$5000+ per week), and employment (full time, parttime/casual and other). Age was categorised into (<55yand ≥55y). Although the standard cut off used to define‘older adults’ is 65 years, the ≥55y group is a useful targetFig. 1 Participant flow diagramAlley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 4 of 11for physical activity interventions to help them to establishhealthy physical activity habits and reduce their risk of dis-ease before they enter into the 65+ age group [35]. Peopleover 55y have lower Internet literacy than younger agegroups and it is therefore likely that they will interact andengage with Web 1.0 and Web 2.0 features differently toyounger adults [25]. Further, the analyses for this studywould be under-powered if the age cut off was higher.Internet self-efficacyParticipants’ confidence in their ability to use the Internetwas measured using the Internet Self-Efficacy Scale (ISES)which has a good validity and internal consistency [36].The ISES uses 8 items assessed on a 7-point Likert scaleto assess a user’s understanding of Internet hardware andsoftware, confidence in gathering information using theInternet and learning skills to use Internet programs, andability to troubleshoot and resolve Internet problems. Themean average of participant’s responses to the 8 items wascalculated as a summary score (range 1–7).Physical activityThe ActiGraph GT3X activity monitor (http://www.theActi-Graph.com) was used to objectively measure minutes ofMVPA and steps per day. The validity and reliability of theActiGraph GT3X has been established [37, 38]. The activitymonitor was worn for 7 full days during waking hours, ex-cept when swimming or bathing and participating in contactsports. Following past research valid wear time was set as atleast 600 min wear time per day on at least 5 days within a7-day time-period [39]. Participants were shown how to wearthe ActiGraph GT3X activity monitor in the induction ses-sion. The ActiGraph GT3X was affixed to an elastic belt andworn on the waist.Anthropometric measurementsHeight and weight was measured by project staff to de-termine BMI. Height and weight was measured with theparticipant standing normally, with feet together andhead in the Frankfurt plane, using Seca 700 mechanicalbalance scales and a Seca 220 measuring rod (SecaGmbH, Hamburg). Participants were asked to removetheir shoes and any heavy personal items/items of cloth-ing prior to measurement.SatisfactionSatisfaction was assessed on a 5-point Likert scale inwhich the Web 1.0 and Web 2.0 participants were askedto indicate how much they agreed with the followingstatements about their website; ‘I can easily find my wayaround,’ ‘I like the overall presentation,’ ‘the informationis useful,’ ‘the information is easy to understand,’ ‘the in-formation is credible,’ ‘it helped me to better monitor myphysical activity,’ ‘it helped me to increase my physicalactivity.’ Responses were summed together to create atotal satisfaction score (range 7–35).UsabilityUsability of each intervention was investigated at eachfollow-up time point using the System Usability Scale(SUS). The SUS includes 10 questions about how easythe website was to use with 5-point Likert scale re-sponses. A summary usability score was calculated(range 0–100). The validity and reliability of the SUS iswell established [40].Website usageWebsite usage for the Web 2.0 and Web 1.0 inter-vention groups was measured using Google analytics.Specifically, the frequency of step log entries, andtime on website (in seconds) and number of visits tothe website were recorded. The number of weeks be-tween baseline and the first occurrence of not enter-ing steps over a two-week period was recorded as thetime for non-usage attrition to occur. These measuresare commonly used to record participants’ engage-ment with websites [32, 41].AnalysisFor aim 1, to test for an interaction between age group(younger, older) and intervention group (Web 2.0 andWeb 1.0) for satisfaction, usability and website usage(website visits, time on site, and days with step entry) at3, 12 and 18 months, 5 generalised linear models werecalculated. A tweedie model with log link was used foreach of the website usage measures due to each beingnegatively skewed and a linear model was used for satis-faction and usability. To test for interactions betweenage group (younger, older) and intervention group (Web2.0 and Web 1.0) on non-usage attrition, a survival ana-lysis was conducted using Cox regression. For these ana-lyses, the Web 1.0 intervention was the referencevariable for intervention group and younger adults wasthe reference variable for age group. Analyses were ad-justed for gender, BMI, education and employment.For aim 2, to test for an interaction between age group(younger, older) and intervention group (Web 2.0, Web1.0 and logbook) for physical activity (MVPA and stepsper day) changes over time (3, 12 and 18 months),intention-to-treat linear mixed models using maximumlikelihood estimation were conducted. An analysis withlogbook as the reference group was conducted to com-pare the Web 2.0 and Web 1.0 groups to the logbookgroup, and another analysis with Web 1.0 as the refer-ence group was conducted to compare the Web 2.0group to the Web 1.0 group. Younger adults were usedas the reference variable for age group and baseline wasused as the reference variable for time. Analyses wereAlley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 5 of 11adjusted for activity monitor wear time, gender, BMI,education and employment.For aim 3, to test for an interaction between Internetself-efficacy scores and intervention group (Web 2.0 andWeb 1.0) for older adults’ satisfaction, usability and web-site usage (website visits, time on site, and days with stepentry) at 3, 12 and 18 months, 5 generalised linearmodels were calculated. A tweedie model with log linkwas used for the website usage measures due to eachbeing negatively skewed and a linear model was used forsatisfaction and usability. To test for an interaction be-tween Internet self-efficacy scores and intervention(Web 2.0 and Web 1.0) for older adults’ non-usage attri-tion, a survival analysis was conducted using Cox regres-sion. The Web 1.0 intervention was the referencevariable for intervention group and analyses were ad-justed for gender, BMI, education and employment.Next, to test for an interaction between Internet self-efficacy scores and intervention group (Web 2.0 andWeb 1.0) for older adults’ physical activity (MVPAminutes per day and steps per day) changes over time(baseline to 3, 12 and 18 months), intention-to-treat lin-ear mixed models were conducted. For each outcomevariable (MVPA minutes per day and steps per day), ananalysis with logbook as the reference group was con-ducted to compare the Web 2.0 and Web 1.0 groups tothe logbook group. Baseline was the reference variablefor time. Activity monitor wear time, gender, BMI,education and employment were included as covariates.ResultsDemographicsBaseline characteristics by age group are presented inTable 1. In total, 65% of participants were female, 34%had a higher education, 46% worked full time, 32%worked as a professional and 30% had an income of over$2000AUD per week. A high percentage was obese(40%). The average age was 51 ± 13 years, the averageminutes of MVPA per day was 24 ± 18 min and the aver-age steps per day was 7248 ± 2424. Internet self-efficacyscores were 5 ± 2 out of 7.Satisfaction, usability and website usageDescriptive statistics of satisfaction, usability and websiteusage and the results of the generalised linear modelsand Cox regression comparing these measures by ageand an interaction between intervention and age arepresented Table 2. Older adults were less likely to have ahigh satisfaction of the interventions compared to theyounger participants, but they were more likely to spendmore time on either website between baseline and3 months. Older adults were also more likely to have ahigher number of days with step entries across all timepoints and spent more time on the websites from 3 to12 months and from 12 to 18 months. A significantinteraction effect demonstrated that time spent on thewebsite in the Web 2.0 compared to the Web 1.0 inter-vention from baseline to 3 months was significantlyhigher for older compared to younger adults.Physical activity changeDescriptive statistics of MVPA minutes per day andsteps per day in older and younger adults in both inter-ventions are presented in Figs. 2 and 3. Results of linearmixed model analyses comparing physical activity overtime by an interaction between intervention group andage are presented in Table 3. A significant interactionTable 1 Baseline characteristics by age groupTotal (N = 504) Younger (n = 299) Older (n = 205)n (%) n (%) n (%)GenderMale 176 (34.9) 91 (30.4) 85 (41.5)Female 328 (65.1) 208 (69.6) 120 (58.5)EducationHigher 171 (33.9) 119 (39.8) 52 (25.4)Trade/diploma 193 (38.3) 114 (38.1) 79 (38.5)School 140 (27.8) 66 (22.1) 74 (36.1)EmploymentFull time 234 (46.4) 168 (56.2) 66 (32.2)Part time 111 (22.0) 73 (24.4) 38 (18.5)Other 159 (31.5) 58 (19.4) 101 (49.3)OccupationaProfessional 159 (31.5) 116 (48.1) 43 (41.3)White collar 102 (20.2) 70 (29.0) 32 (30.8)Blue collar 31 (6.2) 24 (10.0) 7 (6.7)Other 53 (10.5) 31 (12.9) 22 (21.2)Income (AUD)b< $1000 140 (27.8) 119 (39.8) 82 (50.3)$1000–$1999 146 (29.0) 114 (38.1) 46 (28.2)$2000+ 150 (29.8) 66 (22.1) 35 (21.5)BMIUnder/normal 122 (24.2) 80 (26.8) 42 (20.5)Overweight 179 (35.5) 95 (31.8) 84 (41.0)Obese 203 (40.3) 124 (41.5) 79 (38.5)M (SD) M (SD) M (SD)Internet self-efficacy 5.1 (1.5) 5.6 (1.3) 4.4 (1.6)Age 50.8 (13.1) 42.0 (8.8) 63.5 (5.4)Daily Steps 7248 (2424) 7499 (2425) 6893 (2384)Daily MVPA (mins) 24.0 (18.3) 26.7 (1.2) 20.1 (16.2)aMissing n = 159, as only employed participants were asked this questionbMissing n = 68, as some participants chose not to disclose their incomeAlley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 6 of 11between age and intervention was found for MVPA andstep changes from baseline to 3 months, demonstratingthat the Web 2.0 intervention was more effective thanthe logbook at 3 months, and this effect was significantlystronger in older compared to younger adults. No inter-vention group by age group interaction was seen for theWeb 1.0 intervention in comparison to the logbookgroup, nor for the Web 2.0 intervention in comparisonto the Web 1.0 intervention.Internet self-efficacyA t-test revealed that older adults had a significantlylower Internet self-efficacy score out of 7 (M = 4.37, SD= 1.55) compared to younger adults (M = 5.58, SD =1.26); t = (1502) 9.59, p < .001. The variance of Internetself-efficacy scores was also greater in older (2.41) com-pared to younger adults (1.58).Within older adults, a significant interaction betweenolder adults’ Internet self-efficacy and interventiongroup for time on the website was observed at 3 months(OR = 1.11, 95%CI = 1.00–1.51, p = .05), demonstratingthat higher Internet self-efficacy was associated withmore time spent on the website, and this effect was sig-nificantly stronger for the Web 2.0 intervention. Therewere no interactions between older adults’ Internet self-efficacy and intervention group for satisfaction, usability,non-usage attrition, days with a step entry or number ofvisits to the website. Within older adults, no interactionsbetween Internet self-efficacy and intervention group(Web 2.0 and Web 1.0) were found on physical activity(MVPA or step changes) from baseline to 3 months,12 months, or 18 months.DiscussionThis study aimed to determine whether there were agedifferences in intervention satisfaction, usability, websiteusage and effectiveness in a Web 1.0 and Web 2.0 phys-ical activity intervention. The original WALK 2.0 trialTable 2 Satisfaction, usability and website usage by intervention group and age groupWeb 2.0 Web 1.0 Age ComparisonsYounger Older Younger Older Age group Reference= younger adultsIntervention*Age groupReference = younger adultsM (SD) M (SD) M (SD) M (SD) HR (95% CI) HR (95% CI)Satisfaction (min = 1, max = 35)3 months (n = 252) 28.7 (3.7) 27.3 (3.9) 28.7 (4.2) 27.2 (3.1) 0.23 (0.06, 0.87)* 1.13 (0.17, 7.40)12 months (n = 201) 26.9 (4.08) 26.8 (3.7) 27.4 (4.2) 26.4 (3.6) 0.38 (0.08, 1.71) 2.63 (0.31, 22.67)18 months (n = 161) 26.8 (3.5) 25.9 (4.8) 27.2 (4.5) 26.2 (4.0) 0.32 (0.05, 1.93) 1.35 (0.10, 17.70)Usability (System Usability Scale min = 0, max = 100)3 months (n = 252) 67.7 (9.7) 62.9 (10.2) 67.8 (10.4) 64.8 (8.5) 0.05 (0.00, 1.58) 0.18 (0.00, 22.65)12 months (n = 200) 63.6 (9.8) 62.7 (10.7) 64.3 (10.5) 61.0 (9.5) 0.04 (0.00, 1.90) 10.51 (0.04, 2774.51)18 months (n = 160) 63.1 (7.5) 60.6 (12.3) 63.0 (10.7) 61.3 (10.0) 1.19 (0.00, 15.40) 0.41 (0.00, 213.49)Days with step entry (number/week)0–3 months (n = 332) 5.2 (2.0) 5.7 (2.1) 4.6 (2.8) 4.8 (2.3) 1.13 (0.87, 1.47) 1.09 (0.76, 1.57)3–12 months (n = 297) 3.8 (2.8) 5.7 (2.2) 2.8 (3.0) 3.8 (2.9) 1.58 (1.05, 2.39)* 1.18 (0.68, 2.07)12–18 months (n = 213) 2.7 (2.9) 4.5 (2.7) 1.8 (2.8) 3.2 (3.0) 1.83 (1.00, 3.35)* 1.01 (0.44, 2.30)Time on website (seconds/week)0–3 months (n = 332) 596 (622) 1050 (1281) 369 (343) 485 (362) 1.34 (1.01, 1.79)* 1.47 (1.01, 2.14)*3–12 months (n = 297) 229 (279) 618 (689) 124 (218) 212 (277) 2.23 (1.41, 3.54)** 1.53 (0.85, 2.77)12–18 months (n = 213) 148 (293) 335 (313) 59 (97) 163 (336) 2.99 (1.55, 5.78)** 0.81 (0.34, 1.92)Number of visits (number/week)0–3 months (n = 332) 3.7 (2.7) 4.6 (3.0) 1.6 (2.0) 2.1 (1.8) 1.27 (0.95, 1.69) 1.14 (0.79, 1.65)3–12 months (n = 297) 1.9 (2.0) 3.5 (2.6) 0.8 (1.8) 1.0 (1.3) 1.53 (0.97, 2.43) 1.48 (0.83, 2.64)12–18 months (n = 213) 1.6 (2.2) 2.6 (2.2) 0.5 (1.1) 0.7 (1.3) 1.61 (0.81, 3.22) 1.20 (0.51, 2.85)N (%) N (%) N (%) N (%) HR (95% CI) HR (95% CI)Non-usage attrition (n = 332)N (%) stopped using website by 18 months 87 (87.0) 47 (69.1) 86 (86.0) 53 (82.8) 0.83 (0.59, 1.16) 0.66 (0.40, 1.09)*p < .05 **p < .003. Adjusted for gender, BMI, education and employmentAlley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 7 of 11demonstrated higher website usage and behaviourchanges in those who received the Web 2.0 intervention,and the interpretation and discussion of those outcomeshas been published elsewhere [32]. However, age-relateddifferences have not been published or discussed in rela-tion to this study. The current study found that olderadults had more website visits than younger adults, butthis did not differ by intervention. Website usage (i.e.,time on site) was significantly higher for the Web 2.0intervention, and this effect was stronger in the olderadult age group. This finding was unexpected as olderadults in general have a lower use of Web 2.0 featuressuch as social media [42]. It is possible that the olderadults’ spent longer on the Web 2.0 intervention as theyhad more leisure time to interact with the Web 2.0 fea-tures compared to younger participants due to being re-tired. The average age for retirement in Australia is 63,which also was the average age of the older adult group[43]. The older adults may also be more willing to investtime in their health compared to younger adults as theyare at an age where they are at a higher risk of develop-ing chronic diseases [44]. The Web 2.0 features mayhave given them more opportunity to engage comparedto the Web 1.0 website. Alternatively, it could be pos-sible that the older adults took more time to work outhow to use the Web 2.0 features, or used different Web2.0 features than the younger participants [25]. Furtherresearch is needed to investigate how older adults inter-act with specific Web 2.0 features as part of a behaviourchange intervention. Based on the longer time on sitefor older adults in the Web 2.0 intervention, future in-terventions targeting older adults should consider usingWeb 2.0 features to encourage greater website use.No interactions between intervention and age onsatisfaction, usability or non-usage attrition were ob-served, and older adults were less satisfied with bothFig. 3 Steps (per day) by intervention, age group and timeFig. 2 MVPA (minutes per day) by intervention, age group and timeAlley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 8 of 11web-based interventions compared to younger adultsat 3 months. No satisfaction differences were foundat 12 and 18-months, however this may be affectedby low participant numbers at these time points. It isinteresting that the older adults’ satisfaction did notdiffer between the Web 1.0 and the more interactiveWeb 2.0 intervention. Therefore, whilst the Web 2.0features were not problematic for older adults, theydid not improve satisfaction. This finding suggeststhat the low satisfaction was not due to the featuresof either website, but due to the web-based methoditself. Older adults may not be as comfortable asyounger adults in using the Internet for behaviouralinterventions [45], or they may prefer face-to-face so-cial support to help motivate them to increase theiractivity [46]. The lower satisfaction in older adultsmay also be due to the Internet use not being as wellintegrated into their daily lives [20] and should beconsidered when developing physical activity interven-tions for this age group.The results of the current study demonstrated greatereffectiveness of the Web 2.0 intervention compared tothe logbook intervention in older compared to youngeradults, whilst effectiveness of the Web 1.0 interventionin comparison to the logbook intervention did not differby age. The increased effectiveness of the Web 2.0 inter-vention in older compared to younger adults is in linewith past research which found that a web-basedphysical activity intervention providing interactive tai-lored advice was more effective in older compared toyounger adults [21]. This finding may be due to olderadults having more time to engage with interactivewebsite components (over just reading text on a staticpage, which takes less time), which motivated them toincrease their activity further. The older participantsmay have also had more time to be active compared tothe younger participants [47]. Despite the effectivenessof the Web 2.0 intervention compared to the logbookintervention, particularly in older adults, there was nointeraction for age and the Web 2.0 compared to theWeb 1.0 intervention. Therefore we do not know if theWeb 2.0 features significantly impacted behaviourchange. Interactive features that provide peer orcounsellor support improve engagement in behaviourchange interventions across all ages [24], however fur-ther research is needed to investigate which specificWeb 2.0 features are most effective at contributing tobehaviour change in older adults. Furthermore, whilstour results indicate that an intervention with interactivefeatures may be effective at improving short-termintervention effectiveness in older adults, more researchis needed to investigate how such interventions canassist older adults maintain their activity levels in thelong-term.The findings revealed that Internet self-efficacy inolder adults was positively associated with usability rat-ings and intervention satisfaction. The lower levels ofsatisfaction in older adults could therefore be due to thisgroup’s lower levels of Internet self-efficacy. However,Internet self-efficacy was also positively associated witholder adults’ time spent on the website; for which the ef-fect was stronger for those in the Web 2.0 intervention.Therefore, as Web 2.0 features are more complex, theyare likely to be better suited to older adults with a highInternet self-efficacy who have a greater understandingand confidence in using the Internet. Yet, the lowerInternet self-efficacy in older adults was not enough toinfluence the overall effectiveness of the Web 2.0 inter-vention in this age group. These conflicting outcomesmake it difficult to determine the importance of ahigh or low Internet self-efficacy for engagement andbehaviour changes of web-based physical activity in-terventions for older adults, and other studies shouldalso investigate this. On a positive note, Internet self-efficacy within older adults is likely to increase overthe coming years as an increasing number of olderadults have used the Internet for a significant portionof their working lives [42].This study is the first to investigate the effectivenessof a web based physical activity intervention withTable 3 Adjusted mean difference of daily MVPA minutes per day and steps per day over time by an age and intervention groupinteractionTIME*GROUP*AGEaWeb 2.0 vs Log*Older vsYoungerWeb 2.0 vs Web 1.0*Older vsYoungerWeb 1.0 vs Log*Older vsYoungerSTEPS per day 3 months n = 373 1658 (70–3247)* 1354 (−274–2981) 305 (−1331–1940)12 months n = 274 −369 (−2183–1445) −110 (−1986–1767) −259 (−2089–1570)18 months n = 205 −367 (−2554–1820) 59 (−2146–2263) −426 (−2581–1729)MVPA (Mins/day) 3 months n = 373 13.7 (1.1–26.4)* 5.5 (−7.5–18.4) 8.3 (−4.8–21.3)12 months n = 274 2.1 (−12.7–16.9) −2.6 (−17.9–12.7) 4.7 (−10.2–19.6)18 months n = 222 −4.6 (−21.2–11.9) −8.5 (−25.3–8.4) 3.8 (−12.7–20.3)*p < .05. aAdjusted mean difference (95% CI) compared to baseline. Adjusted for monitor wear time, gender, BMI, education and employmentAlley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 9 of 11interactive Web 2.0 features compared to a Web 1.0and logbook intervention in older adults. The findingsare important for informing the next generation ofweb-based interventions with a wide reach for olderadults. Strengths of the study include the objectivephysical activity measures, the large sample and thelong-term follow up. However due to the nature ofthe RCT with the long term follow up, and thequickly advancing Internet technology, the Web 2.0technology used in the intervention may already beoutdated to some extent. Further, attrition may haveaffected the ability to detect satisfaction, usability andwebsite usage differences by age and intervention at12 and 18 months and biased physical activity out-comes at 12 and 18 months. Lastly, the number ofolder adults over the age of 65 was small, whichrequired an age cut-off of 55 to maintain adequatestatistical power. Further research is needed to testthe effectiveness of web-based physical activity inter-ventions in older adults with a larger cohort of olderadults to allow further age break down (e.g. 55–65and 65+) and investigate the influence on other fac-tors relevant to older adults including chronic diseasestatus and retirement status.ConclusionThe findings demonstrate that web-based physical activityinterventions can be more engaging and effective in oldercompared to younger adults, and that interventions withWeb 2.0 features are particularly engaging and effective inolder adults. Although the Web 2.0 intervention was notas engaging in older adults with a low Internet self-efficacy, Internet self-efficacy was not associated witholder adults’ physical activity changes. Future web-basedinterventions targeting older adults are recommended toinclude Web 2.0 features to improve website usage andoptimise physical activity outcomes.Additional filesAdditional file 1: CONSORT checklist. (DOC 218 kb)Additional file 2: Participants in Walk Trial. (DOCX 12 kb)Additional file 3: TIDieR checklist of the interventions. (PDF 129 kb)AbbreviationsISES: Internet Self-Efficacy Scale; MVPA: Moderate-to-vigorous PA; SUS: SystemUsability ScaleAcknowledgementsNot applicable.FundingThe study was funded through a project grant from the National Health andMedical Research Council (NHMRC), Australia. The funding body had noinvolvement in the design of the study and collection, analysis, andinterpretation of data or in writing the manuscript.Availability of data and materialsThe datasets used and/or analysed during the current study are availablefrom the corresponding author on reasonable request.Authors’ contributionsSJA conducted the data analysis and wrote the manuscript. All otherAuthors, GSK, MJD, CMC, TNS, AJM, RRR, RT, AKV, WKM & CV designed thetrial, collected data and revised the manuscript. In addition, GSK oversaw thetrial, AKV managed the data, WKM received funding and CV conceived thisstudy. All authors read and approved the final manuscript.Ethics approval and consent to participateThe study received ethics approval from the Human Research EthicsCommittees of the Western Sydney University (H8767) and CentralQueensland University (H11/01–005). All participants provided writtenconsent before data collection.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Physical Activity Research Group, Appleton Institute, School of Health,Medical and Applied Sciences, Central Queensland University, Rockhampton,QLD 4702, Australia. 2School of Science and Health, Western SydneyUniversity, Sydney, NSW 2751, Australia. 3School of Medicine and PublicHealth, Priority Research Centre for Physical Activity and Nutrition, Faculty ofHealth and Medicine, University of Newcastle, Callaghan, NSW 2308,Australia. 4School of Health and Exercise Science, University of BritishColumbia, Kelowna, BC V1V 1V7, Canada. 5Griffith University, School of AlliedHealth Sciences, Gold Coast, QLD 4222, Australia. 6School of Health Science,Flinders University, Adelaide, SA 5042, Australia. 7Department of Food,Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS 66506,USA. 8School of Computing, Engineering and Mathematics, Western SydneyUniversity, Sydney, NSW 2560, Australia. 9Faculty of Physical Education andRecreation, University of Alberta, Edmonton, AB T6G 2H9, Canada.Received: 12 September 2017 Accepted: 21 December 2017References1. 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Evidence froma large Australian study Am J Prev Med. 2016; https://doi.org/10.1016/j.amepre.2016.01.019.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Alley et al. International Journal of Behavioral Nutrition and Physical Activity  (2018) 15:4 Page 11 of 11

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