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Does parental and adolescent participation in an e-health lifestyle modification intervention improves… Tu, Andrew W; Watts, Allison W; Chanoine, Jean-Pierre; Panagiotopoulos, Constadina; Geller, Josie; Brant, Rollin; Barr, Susan I; Mâsse, Louise Apr 24, 2017

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RESEARCH ARTICLE Open AccessDoes parental and adolescent participationin an e-health lifestyle modificationintervention improves weight outcomes?Andrew W. Tu1, Allison W. Watts2, Jean-Pierre Chanoine3, Constadina Panagiotopoulos3, Josie Geller4, Rollin Brant5,Susan I. Barr6 and Louise Mâsse7*AbstractBackground: Few studies have evaluated the effect of adherence to a lifestyle intervention on adolescent healthoutcomes. The objective of this study was to determine whether adolescent and parental adherence tocomponents of an e-health intervention resulted in change in adolescent body mass index (BMI) and waistcircumference (WC) z-scores in a sample of overweight/obese adolescents.Methods: In total, 159 overweight/obese adolescents and their parents participated in an 8-month e-health lifestyleintervention. Each week, adolescents and their parents were asked to login to their respective website and tomonitor their dietary, physical activity, and sedentary behaviours. We examined participation (percentage ofwebpages viewed [adolescents]; number of weeks logged in [parents]) and self-monitoring (number of weeksbehaviors were tracked) rates. Linear mixed models and multiple regressions were used to examine change inadolescent BMI and WC z-scores and predictors of adolescent participation and self-monitoring, respectively.Results: Adolescents and parents completed 28% and 23%, respectively, of the online component of theintervention. Higher adolescent participation rate was associated with a decrease in the slope of BMI z-score butnot with change in WC z-score. No association was found between self-monitoring rate and change in adolescentBMI or WC z-scores. Parent participation was not found to moderate the relationship between adolescentparticipation and weight outcomes.Conclusions: Developing strategies for engaging and promoting supportive interactions between adolescentsand parents are needed in the e-health context. Findings demonstrate that improving adolescents’ adherence toe-health lifestyle intervention can effectively alter the weight trajectory of overweight/obese adolescents.Keywords: Adherence, e-health, Obesity, Parenting, Lifestyle interventionBackgroundChildhood obesity continues to be a worldwide epidemic[24, 41] and is associated with significant health issues,including metabolic, cardiovascular, gastrointestinal,pulmonary, orthopedic, and psychological disorders, inadulthood [23]. Among Canadian youth, 31.1% are over-weight or obese and 11.6% are obese [31]. Family-basedinterventions that target physical activity (PA), sedentary,and dietary behaviours have had some success at treatingchildhood obesity [19, 26]. However, there is a need toimprove the efficacy of these interventions among ado-lescents as they have only demonstrated modest andshort-term effects (on average, −0.14 change in bodymass index (BMI) z-score at 12 months follow-up) [26].Web-based or e-health interventions delivered throughthe internet are a potentially cost-effective and promis-ing method for delivering adolescent weight manage-ment interventions as the majority of households (atleast 83% of Canadian and U.S. households) have accessto the internet [14, 34]. E-health interventions have beenshown to be at least as effective as traditional non-web-* Correspondence: lmasse@cfri.ubc.ca7BC Children’s Hospital Research Institute, School of Population and PublicHealth, University of British Columbia, 2206 East Mall, Vancouver, BC V6T 1Z3,CanadaFull list of author information is available at the end of the article© The Author(s). 2017 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.Tu et al. BMC Public Health  (2017) 17:352 DOI 10.1186/s12889-017-4220-0based interventions [42] and evidence regarding theirefficacy to treat or prevent childhood obesity is stillemerging [10, 11, 15]. However, adherence (defined asattendance and utilization of intervention components)to e-health interventions varies greatly. In a systematicreview of e-health interventions among adults, anaverage adherence rate of 50% was documented, with awide range reported across studies (10 to 90%) [18, 34].Adherence to lifestyle interventions has consistentlypredicted reduction in BMI z-score in children [13, 36]and recently has been identified as the single mostimportant factor to target for increasing the success ofsuch interventions [27].E-health interventions often consist of several compo-nents (e.g., educational materials, self-monitoring, goal-setting, etc.) and the extent to which adherence to thesecomponents affect change in BMI among adolescents isunknown. In one study, adolescents who utilized moreself-monitoring components of a lifestyle intervention(i.e., tracking food and beverage intake) achieved greaterreductions in BMI z-score [32]. In contrast, other studiesof adolescents found no change in BMI z-score with at-tendance and utilization of program components [28].Parent participation in interventions can also affectadolescents’ adherence and result in greater weight loss[35, 38, 46]. For instance, when mothers regularlyattended treatment sessions with their daughters in a16-week behavioral weight control program for Blackadolescent girls, daughters lost significantly more weightthan when mothers did not attend the sessions [38].Although several e-health weight management interven-tions for adolescents have involved parental participation,few have evaluated the effect of parental participation onadolescent adherence [1].In summary, parental and child adherence appearsimportant to improve the efficacy of pediatric weightmanagement interventions. However, different compo-nents may have different effects on weight outcomesand the role of parental participation on adolescentadherence has not been well studied in the e-healthcontext. Therefore, this study examined: 1) whetheradolescent adherence to specific components of an e-health lifestyle modification intervention (participationand self-monitoring) predicted change in BMI and waistcircumference (WC) z-scores and 2) whether parentalparticipation and self-monitoring moderated the rela-tionship between adolescent adherence and change inBMI and WC z-scores. It was hypothesized that adoles-cents who had higher participation and utilized moreself-monitoring tools would have a greater reduction inBMI and WC z-scores compared to adolescents whohad lower participation and utilized less self-monitoringtools. In addition, an even greater reduction in BMI andWC z-scores was expected among adolescents with ahigh participating parent compared with a low partici-pating parent.MethodsSampleAdolescents and one corresponding parent were re-cruited through online and paper advertisements (68%),by mailing invitations to patients of the British Columbia(BC) Children’s Hospital Endocrinology and DiabetesClinic (14%), previous participants of the Centre forHealthy Weights program in BC (14%) and by othersources (4%). Adolescents were eligible if they were be-tween the ages of 11 and 16 years and overweight orobese (defined as >1 Standard Deviation (SD) for ageand sex using the World Health Organization (WHO)cut-offs) [44]. The adolescent and their parent had to beliving in the Metro Vancouver area, not planning tomove during the study and be literate in English. Ado-lescents were excluded if they were diagnosed with type1 diabetes, had other comorbidities requiring medicalattention, had health problems or disabilities precludingthem from being physically active, had a history of adiagnosed psychiatric condition or substance abuse, wereenrolled in another behavioural change or weight lossintervention, or were using medications that affectedbody weight. One parent had to volunteer to participatein the study, the family self-selected which parent wasable to attend all in-person assessments. Of the 454parent-adolescent dyads that responded to the advertise-ments or invitations and were contacted, 227 declinedand 51 did not meet the eligibility criteria. Theremaining 176 (38.8%) families were invited to attendand completed the initial baseline assessment −16dropped out after baseline assessment and were neverbriefed about the intervention and one family wasexcluded from the analyses due to a death within thefamily leaving 159 families included in the analyses.Data collection protocolThis study was approved by the University of BritishColumbia Children’s and Women’s Research EthicsBoard and by the University of Waterloo’s ResearchEthics Board. Parent-adolescent dyads who met eligibil-ity criteria and expressed interest in the study receivedthe consent form via email and attended an in-personmeeting at an evaluation centre. At the initial meeting,the parent-adolescent dyads reviewed and signed theconsent forms and completed the baseline question-naires. One to two weeks later, the dyads returned to theevaluation centre and were introduced to the MySteps®intervention, provided with login information, givenpedometers to monitor their steps, and shown how torecord and track their behaviours (steps, sedentary be-haviours, and dietary intake). Parent-adolescent dyadsTu et al. BMC Public Health  (2017) 17:352 Page 2 of 9were instructed to login every week for a period of eightmonths with new content being available every Sunday.Follow-up assessments were scheduled at 4- and 8-months, and all data were collected from December2010 to March 2013.InterventionThe intervention has been described elsewhere [20].Briefly, the MySteps® intervention adapted the adolescentPACE e-health intervention [28, 29] to the Canadiancontext –aligning the intervention with the Canada FoodGuide [16] and recommendation for PA [37]. MySteps®included individualized and familial web-based weightmanagement information for adolescents and their par-ents based on the Chronic Care Model, [39]. Social Cogni-tive Theory, [5] and the Transtheoretical Model of Change[30]. From these perspectives, the MySteps interventionincluded motivational counseling via email and telephonecontact, skill building techniques (including goal setting,self-monitoring, and social support techniques), tailoredinteractions, targeted known mediators of behaviorchange (self-efficacy, barriers, enjoyment, goal setting, andsocial support), and referral to community resources.The 8-month (34 weeks) web-based intervention con-sisted of weekly logins to a website that encouragedhealthy eating, PA, and reduced screen time. For the first17 weeks, adolescents were expected to login on aweekly basis and received new topics, challenges, andskills to help them change their behaviours. The inter-vention started by assessing each adolescent’s currentbehaviours and then developing an action plan based ontheir initial behaviours. During the first 17 weeks, ado-lescents learn the benefits of improving their healthbehaviours and set behaviour change goals. In addition,the website allowed them to track their steps, diet, andscreen time. For the remaining 17 weeks, adolescentswere still expected to login on a weekly basis; however,they were allowed to choose the behaviours and skillsthey worked on. The parents were asked to login to adifferent website and each week they received comple-mentary topics and challenges designed to support theirchild’s challenges of the week. Parents also received bi-weekly emails with information about how to help andencourage their adolescents to change their healthbehaviours and create a supportive environment. Bothparents and adolescents were given a one-week breakfrom logging in to the website, on week 23, but wereencouraged to practice what they learned. Weeklyreminders to login to the website were emailed toparents and adolescents. In addition, adolescents com-pleted counselling sessions via telephone (5–10 min induration) on weeks 2, 4, 8, 12, and 16. Finally, both par-ents and adolescents were provided with pedometers atthe start of the intervention to track their behaviors.MeasuresParticipation rateFor adolescents, participation rate was defined as themean percentage of webpages viewed per week, where atotal of 83 and 78 pages could be viewed in the first andlast 4-months, respectively (typically there were four tofive pages per week to view). Parental participation ratewas defined as the percentage of weeks the parentslogged in to their website over the study period.Self-monitoringParent and adolescent self-monitoring rates were definedas the number of weeks parents and adolescents, respect-ively, tracked either their diet (e.g., consumption of fruitand vegetables, fast foods, and sugar-sweetened bever-ages), steps, or TV/computer usage divided by the totalnumber of weeks in the study period. The tracking toolswere incorporated in the MySteps® and participants hadaccess to the tracking tools as soon as they began theintervention and were prompted throughout the interven-tion to utilize them. In the first 16 weeks of the interven-tion they were prompted 11 times to use the internaltracking forms and for the remaining of the programtracking prompts depended on the behavior adolescentchose to work on. Participants needed to have recordedinformation on at least one of the three activities on atleast one of the seven days of the week to be counted ashaving tracked (yes/no) that particular week.Anthropometric measureHeight, weight, and WC were measured for adolescentsand the corresponding parent at baseline, 4-months, and8-months. Measurements were taken twice at each visitusing a stadiometer (Hohltain, United Kingdom) forheight, a calibrated scale (Model 597 K, Health-o-meter,McCook, Illinois) for weight, and a measuring tape(Hoechstmass, Germany) for WC. BMI (kg/m2) was deter-mined by dividing the average weight (kg) with the aver-age height (m) squared. BMI z-scores were derived from aStata macro developed by the WHO for children and ado-lescents aged 5 to 19 (World Health Organization [45]).WHO cutoffs for overweight and obesity were used to de-scribe the weight status of adolescents (>1 standard devi-ation (SD) = overweight; >2 SD = obesity) and parents(<18.5 as underweight, 18.5 to 24.9 as normal weight, 25to 29.9 as overweight, and > = 30 as obese). WC z-scoreswere calculated using Canadian data [17].DemographicsAge, gender, ethnicity, household income, and maternaleducation data were collected from parents at baselineusing adapted questions from the Canadian CommunityHealth Survey [33]. Parents were asked to select theircultural and racial background from a list of 13 ethnicTu et al. BMC Public Health  (2017) 17:352 Page 3 of 9categories. Responses were re-categorized to: 1) White; 2)Chinese/South East Asian; 3) South Asian; 4) Aboriginal;and 5) other. Maternal educational and household incomewere grouped into categories as displayed in Table 1.AnalysisLinear mixed models were conducted to assess the effectof adolescent participation and self-monitoring rates onchange in adolescent BMI and WC z-scores over time.Adolescent participation rate and self-monitoring wereanalyzed separately for the two outcome variables. In-cluded in each model were time (as a random effect), aninteraction between time and the main independentvariable (participation rate or self-monitoring) control-ling for all baseline socio-demographic variables (i.e.,adolescent age and gender and household income, ma-ternal education, and ethnicity). Linear mixed models al-lows for the inclusion of all available data regardless ofthe amount of missing data. Analyses of the data usingmultiple imputation techniques found similar resultsand therefore are not reported. The moderating effect ofparent participation was examined by including allthree-way and two-way interaction terms betweenparent participation, adolescent participation and time.Multivariable regression analyses were conducted toassess predictors of adolescent adherence and self-monitoring rates using all socio-demographic variablesand parental participation and self-monitoring. Allanalyses were conducted using Stata v.13.1.ResultsSample characteristics are displayed in Table 1. Of theadolescents, 81% were obese. In contrast, 34% of parentswere overweight and 41% were obese. Of the 33 weeksthat adolescents and parents were asked to login to theirrespective websites, adolescents logged into the websitean average of 13.4 weeks, and parents logged into thewebsite an average of 7.5 weeks (Table 2).Table 2 displays the mean and median participationand self-monitoring rates of adolescents and parents. Onaverage, adolescents and parents completed 28% (pro-portion of web-pages viewed) and 23% (proportion ofweeks logged in) of the intervention, respectively. Theparticipation rate was significantly higher during the first4 months than the last 4 months for both adolescents(38% vs. 18%) and parents (31% vs. 14%). Fifteen (9.4%)adolescents and 50 parents (31.5%) did not login to theintervention website during the entire study period. Onaverage, adolescents and parents entered self-monitoringdata on at least one day for at least one behaviour for24% and 13% of the weeks during the study period,respectively. Forty-one adolescents (26%) and 81 parents(51%) did not enter any self-monitoring informationduring the entire study period.In multivariable regression analyses, parent adherencerate was significantly associated with adolescent participa-tion rate such that a 10% increase in parent participationrate was associated with a 6.1% increase in adolescent par-ticipation rate (Table 3). Similarly, parent self-monitoringwas significantly associated with adolescent self-monitoringsuch that a 10% increase in parent self-monitoring wasassociated with a 5.4% increase in adolescents self-monitoring.Table 4 displays the results of the linear mixed modelspredicting change in BMI z-score and WC z-score. Asignificant time-by-participation rate interaction(p < 0.01) effect was found on BMI z-score such thatadolescents with high levels of participation had a de-creasing trajectory of BMI z-score and those with lowlevels of participation had a stable or increasing trajec-tory of BMI z-score. Differences in BMI z-score trajec-tory by participation rate can be found in Fig. 1. Adecreasing trajectory started at participation ratesgreater than 10%. No other significant interaction effectwas found among the other models. The inclusion ofTable 1 Socio-demographic characteristics of participants(n = 159)Adolescent ParentaMean (SD) age 13.2 (1.8) 45.8 (6.2)Gender [n (%)]Male 68 (42.8) 24 (15.1)Female 91 (57.2) 135 (84.9)Mean (SD) baseline BMI (kg/m2) 30.7 (5.9) 30.0 (7.2)Mean (SD) baseline BMI z-score 2.67 (0.82)Mean (SD) baseline waist circumference (cm) 93.3 (13.8) 92.1 (16.3)Mean (SD) baseline waist circumference z-score 4.37 (2.05)Parent ethnicity [n (%)]White 75 (48.1)East/Southeast Asian 23 (14.7)South Asian 20 (12.8)Aboriginal 13 (8.3)Other 25 (16.0)Household income [n (%)]≤ $40,000 29 (18.8)$40,001–$80,000 47 (30.5)$80,001–$120,000 44 (28.6)$120,001+ 34 (22.1)Maternal education [n (%)]High school or less 30 (19.1)Trade certificate/diploma 57 (36.3)Bachelor’s degree 33 (21.0)Above bachelor’s 37 (23.6)aNumbers may not add up to total N due to missing dataTu et al. BMC Public Health  (2017) 17:352 Page 4 of 9parent participation as an interaction term with adoles-cent participation and time was not significant and wastherefore left out of the model.DiscussionThis study found that adolescents who had greaterparticipation in the e-health lifestyle intervention had agreater decrease in their BMI z-score trajectory at 8-months than those who participated less. Specifically, ado-lescents who viewed more than 10% of the interventionmaterials stabilized their BMI z-score trajectories but thosewho viewed more content saw a greater decrease in theirBMI z-score trajectories – e.g., viewing 50% of the contentresulted in an average BMI z-score reduction of 0.1 at 8-months. This represents the first web-based interventionstudy among adolescents that documented a change inBMI z-score as previous studies found attendance orutilization of program components resulted in change inhealth behaviours (PA and nutrition outcomes) without achange in BMI z-score [15, 28, 43]. The short duration ofprevious e-Health interventions may explain why weightchange was not observed in past studies [15]. In addition,parent participation did not appear to moderate the effectof adolescent participation on weight outcomes. Finally,counter to what others have found, [12, 21, 25, 28]utilization of the self-monitoring tools by either parents oradolescents was not related to adolescents’ change in BMIor WC z-scores. It may be that the self-monitoring toolsutilized in this intervention were not as engaging as thoseincluded in other studies as a high percentage of adoles-cents and parents did not use them (26% and 51%,respectively).Contrary to expectations, parental participation didnot influence the outcomes of the intervention as it didnot moderate adolescents change in BMI z-score eventhough parental participation was associated with ado-lescents’ participation. This finding contradicts whatothers have found as family-based intervention have im-proved the success of traditional clinical interventions[22, 26]. However, these interventions focused on pre-adolescents or younger children, targeted the parents,and were delivered in person and in group-based set-tings and ensured that parents were actively engaged inthe intervention [3]. One possible mechanisms throughwhich parental participation could have influenced theoutcomes of the current intervention is by changing thehome environment to better support their adolescent’shealth behaviors, [11] as the parent site provided themwith advice on how to support the challenges and goalstheir adolescents were expected to achieve that week.While this e-health intervention targeted both the ado-lescents and their parents, the e-health context may beless successful at engaging and promoting supportive in-teractions between adolescents and their parents. WhileTable 2 Study participation statistics for adolescents andparentsMean (SD) Median (IQR)aAdolescentNumber of weeks logged into website 13.4 (11.4) 11 (3–24)Percent of weeks logged into website 40.5 (34.6) 33.3 (9.1–72.7)Number of weeks self-monitoring wasentered8.3 (9.9) 4 (0–13)Percent of weeks self-monitoring wasentered24.3 (29.2) 11.8 (0–38.2)Participation RateBaseline to 4 months 38.1 (32.3) 33.2 (7.1–68.2)4 months to 8 months 18.0 (24.2)b 4.7 (0–35.0)Baseline to 8 months 28.4 (26.8) 20.5 (4.8–46.5)Self-monitoring RateBaseline to 4 months 36.3 (36.2) 23.5 (0–70.6)4 months to 8 months 12.4 (27.3)b 0 (0–5.9)Baseline to 8 months 24.3 (29.2) 11.8 (0–38.2)ParentNumber of weeks logged into website 7.5 (9.7) 3 (0–12)Percent of weeks logged into website 22.3 (29.5) 9.1 (0–36.4)Number of weeks self-monitoring wasentered4.5 (8.3) 0 (0–5)Percent of weeks self-monitoring wasentered13.2 (24.5) 0 (0–14.7)Participation RateBaseline to 4 months 30.9 (34.7) 17.6 (0–58.8)4 months to 8 months 14.2 (27.8)b 0 (0–6.3)Baseline to 8 months 22.8 (29.5) 9.1 (0–36.4)Self-monitoring RateBaseline to 4 months 18.5 (29.3) 0 (0–23.5)4 months to 8 months 8.0 (23.1)b 0 (0–0)Baseline to 8 months 13.2 (24.5) 0 (0–14.7)aInter-quartile rangebSignificantly different compared to baseline to 4 monthsTable 3 Predictors of adolescent participation and self-monitoringratesaAdherence Self-monitoringb SE β b SE βParent participation 0.606** 0.069 0.662 0.016 0.085 0.016Parent self-monitoring −0.038 0.081 −0.035 0.535** 0.101 0.450aModels adjusted for baseline adolescent age, gender, BMI z-score, and waistcircumference z-score, parental ethnicity, maternal education, andhousehold income*p < 0.05; **p < 0.01Tu et al. BMC Public Health  (2017) 17:352 Page 5 of 9parents can influence their adolescents’ participation inthe intervention, the approach they use to achieve thismay not result in the desired effect as observed in thisstudy. For example, if parents are pressuring theiradolescents to participate it can explain why parentalparticipation did not influence the outcomes of theintervention as controlling strategies have been found tobe less effective than autonomy supportive approachesin achieving desired health behaviours [7]. For example,a number of observational and longitudinal studies haveshown that parents who use controlling practices suchas pressuring the child to eat healthier food result inpoor self-regulatory behaviours such as eating in theabsence of hunger [8, 9]. Previous studies emphasize theimportance of supporting change in children’s healthbehaviours at the household level; [3, 22] however, thisstudy suggests the need in helping parents use moreautonomy supportive parenting practices as it mayexplain why their engagement did not positivelyinfluence the outcome of the intervention. Future e-health interventions should better support parent/ado-lescent interactions, such as providing parents with theskills to use autonomy supportive approaches as a wayto better support their adolescent and ultimately im-prove the efficacy of these interventions.As internet access in households is becoming increas-ingly common (in 2012 at least 83% of Canadian andU.S., households had internet at home), [34] onlinedelivery of interventions has the potential to broadlyimprove health outcomes among a wider segment of thepopulation [2, 4, 6]. However, participation rates foronline interventions have been quite variable [18]. Over-all, participation in the present e-health intervention wassub-optimal for both adolescents and parents and ap-pears much lower than other web-based interventionsthat report a 50% participation rate on average [18].However, it is somewhat difficult to compare participa-tion rates across studies as most use login rates versuspercent of content viewed which is what was reportedfor the adolescents in the current study. The login ratefor this study, defined as the number of weekly loginsover the total number of weeks of the study, was 40.5%for the adolescents and 22.3% for the parents. Inaddition, participation rates are likely higher in shorterinterventions than in longer interventions. In fact, thisstudy found much higher participation rates in the firstfour months of the intervention than in the last fourmonths. Nonetheless, strategies to improve participationof adolescents may benefit e-health interventions.Utilization of the self-monitoring tools that were partof the e-health intervention was not found to have aneffect on adolescents BMI z-score trajectory, eventhough there is evidence that such strategy is importantTable 4 Results from linear mixed-models predicting BMI z-score and waist circumference z-scoreaBMI z-score Waist circumference z-scoreParticipation Self-monitoring Participation Self-monitoringFixed effectsIntercept 3.381 (0.536)** 3.400 (0.533)** 4.041 (1.291)** 4.153 (1.286)**Time (weeks) 0.001 (0.001) −0.002 (0.001) 0.000 (0.008) −0.010 (0.007)Participation rate (%) −0.124 (0.231) −0.212 (0.578)Time x Participation rate −0.008 (0.003)** −0.018 (0.017)Self-monitoring rate (%) −0.290 (0.210) −0.711 (0.525)Time x Self-monitoring rate −0.002 (0.003) 0.009 (0.014)Random effectsIntercept standard deviation 0.721 (0.042) 0.718 (0.042) 1.631 (0.123) 1.618 (0.122)Slope standard deviation 0.007 (0.001) 0.007 (0.001) 0.027 (0.007) 0.027 (0.007)Residual standard deviation 0.116 (0.008) 0.116 (0.008) 0.868 (0.063) 0.869 (0.064)aTable displays coefficients and standard errors (in parentheses); Models adjusted for baseline adolescent age, adolescent gender, household income, maternaleducation, and ethnicity*p < 0.05; **p < 0.01Fig. 1 Trajectories of BMI z-score over the study period byparticipation rateTu et al. BMC Public Health  (2017) 17:352 Page 6 of 9to include in interventions [12, 21, 25, 28]. This study doc-umented utilization of the self-monitoring tools by deter-mining whether the participants entered information intothe e-health program; however, both parents and adoles-cents could have used alternative ways of self-monitoringwhich was not captured by the e-health program (such asusing the technique without actually entering the informa-tion in the program). This e-health program had differenttracking forms for each behavior (steps, screen time, anddietary behaviours) and participants were not expected touse them every week. Perhaps using a single tracking formand setting the expectation that it be used every weekwould have had a different impact on the outcomes andcan partly explain the findings and discrepancies withothers studies [12, 21, 25, 28].There are several limitations to this study. First, thesample included families with overweight and obeseadolescents that volunteered to participate in the inter-vention limiting the generalizability to this populationwhich is typical of similar studies. Second, adolescent’sparticipation measured whether they viewed the contentbut it could not be determined whether they fully readthe content. Third, this study did not include a controlgroup; therefore, the effect of the intervention on changein BMI z-score must be taken with caution. However,participants that had higher participation rates showed agreater reduction in BMI z-score indicating a potentialeffect from the intervention. Lastly, the study did nottake into account self-perceived weight status which hasbeen shown to be associated with intention to preventweight gain [40].ConclusionsIn conclusion, this study found a dose-response relation-ship between adolescent participation to an e-health life-style intervention with BMI trajectory – finding a greaterdecrease in BMI z-score among overweight or obese ado-lescents with increased participation. Parental participa-tion influenced their adolescents’ participation; however, itdid not influence their adolescents’ reduction in BMI.Future interventions could benefit from exploring poten-tial mechanisms to improve adherence of adolescents ine-health interventions. With improved adherence, theseresults suggest that e-health lifestyle interventions can bean effective strategy to beneficially alter the weight trajec-tory of overweight and obese adolescents. In addition, thisstudy highlights the need to develop strategies thatpromote both active and supportive engagement of par-ents in e-health interventions as a way to further increasethe efficacy of these interventions.AbbreviationsBC: British Columbia; BMI: Body mass index; PA: Physical activity; SD: Standarddeviation; WC: Waist circumference; WHO: World Health OrganizationAcknowledgementsThe authors wish to thank Maria Valente and Judith de Niet for helpingcollect the data for this study.FundingThe data collection for this study was funded by a peer-reviewed grant thatLCM received from the Canadian Institutes of Health Research Institute (CIHR)of Nutrition, Metabolism and Diabetes and the Health Research Foundation(Funding Reference Number 92369). In addition, during the period of thestudy, LCM received salary support from the Michael Smith Foundation forHealth Research and the BC Children’s Hospital Research Institute (BCCHRI).AWW received a doctoral scholarship from CIHR in partnership with theDanone Institute of Canada, and from the Heart and Stroke Foundation ofCanada and the CIHR Training Grant in Population Intervention for ChronicDisease Prevention: A Pan-Canadian Program (Grant #53893). AWT received ascholarship from a CIHR Doctoral Research Award and post-doctoral supportfrom BCCHR and the Michael Smith Foundation for Health Research.Availability of data and materialsThe de-identified datasets analyzed in this study is available from thecorresponding author on reasonable request.Authors’ contributionsAWT analysed and interpreted the data and drafted the manuscript. AWWcollected and interpreted the data and edited the manuscript. JPC, CP, JG,RB, and SIB conceived the study and edited the manuscript. LCM obtainedfunding, conceived the study, interpreted the data, and edited themanuscript. Each author contributed to further development and revisionsof the manuscript and approved the final submission.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participateThis study was approved by the University of British Columbia Children’s andWomen’s Research Ethics Board and by the University of Waterloo’s ResearchEthics Board. At the initial meeting, families reviewed and signed theconsent forms.Publisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1School of Population and Public Health, University of British Columbia, 2206East Mall, Vancouver, BC V6T 1Z3, Canada. 2School of Public Health,University of Minnesota, 1300 South Second St, Suite 300, Minneapolis, MN55454, USA. 3Department of Pediatrics, University of British Columbia, 4480Oak Street, Vancouver, BC V6H 3V4, Canada. 4Department of Psychology,University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC V6T 2A1,Canada. 5Department of Statistics, University of British Columbia, 4480 OatStreet, Vancouver, BC V6H 3V4, Canada. 6Food Nutrition and Health,University of British Columbia, 2205 East Mall, Vancouver, BC V6T 1Z4,Canada. 7BC Children’s Hospital Research Institute, School of Population andPublic Health, University of British Columbia, 2206 East Mall, Vancouver, BCV6T 1Z3, Canada.Received: 7 May 2016 Accepted: 1 April 2017References1. 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J PediatrPsychol. 2013;38:766–74.•  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:Tu et al. BMC Public Health  (2017) 17:352 Page 9 of 9

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