J. Med. Internet Res. - Features predicting weight loss in overweight or obese participants in a web-based intervention: randomized trial.


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CKGROUND: Obesity remains a serious issue in many countries. Web-based programs offer good potential for delivery of weight loss programs. Yet, many Internet-delivered weight loss studies include support from medical or nutritional experts, and relatively little is known about purely web-based weight loss programs.OBJECTIVE: To determine whether supportive features and personalization in a 12-week web-based lifestyle intervention with no in-person professional contact affect retention and weight loss.METHODS: We assessed the effect of different features of a web-based weight loss intervention using a 12-week repeated-measures randomized parallel design. We developed 7 sites representing 3 functional groups. A national mass media promotion was used to attract overweight/obese Australian adults (based on body mass index [BMI] calculated from self-reported heights and weights). Eligible respondents (n = 8112) were randomly allocated to one of 3 functional groups: information-based (n = 183), supportive (n = 3994), or personalized-supportive (n = 3935). Both supportive sites included tools, such as a weight tracker, meal planner, and social networking platform. The personalized-supportive site included a meal planner that offered recommendations that were personalized using an algorithm based on a user's preferences for certain foods. Dietary and activity information were constant across sites, based on an existing and tested 12-week weight loss program (the Total Wellbeing Diet). Before and/or after the intervention, participants completed demographic (including self-reported weight), behavioral, and evaluation questionnaires online. Usage of the website and features was objectively recorded. All screening and data collection procedures were performed online with no face-to-face contact.RESULTS: Across all 3 groups, attrition was high at around 40% in the first week and 20% of the remaining participants each week. Retention was higher for the supportive sites compared to the information-based site only at week 12 (P = .01). The average number of days that each site was used varied significantly (P = .02) and was higher for the supportive site at 5.96 (SD 11.36) and personalized-supportive site at 5.50 (SD 10.35), relative to the information-based site at 3.43 (SD 4.28). In total, 435 participants provided a valid final weight at the 12-week follow-up. Intention-to-treat analyses (using multiple imputations) revealed that there were no statistically significant differences in weight loss between sites (P = .42). On average, participants lost 2.76% (SE 0.32%) of their initial body weight, with 23.7% (SE 3.7%) losing 5% or more of their initial weight. Within supportive conditions, the level of use of the online weight tracker was predictive of weight loss (model estimate = 0.34, P < .001). Age (model estimate = 0.04, P < .001) and initial BMI (model estimate = -0.03, P < .002) were associated with frequency of use of the weight tracker.CONCLUSIONS: Relative to a static control, inclusion of social networking features and personalized meal planning recommendations in a web-based weight loss program did not demonstrate additive effects for user weight loss or retention. These features did, however, increase the average number of days that a user engaged with the system. For users of the supportive websites, greater use of the weight tracker tool was associated with greater weight loss.

Resumo Limpo

ckground obes remain serious issu mani countri webbas program offer good potenti deliveri weight loss program yet mani internetdeliv weight loss studi includ support medic nutrit expert relat littl known pure webbas weight loss programsobject determin whether support featur person week webbas lifestyl intervent inperson profession contact affect retent weight lossmethod assess effect differ featur webbas weight loss intervent use week repeatedmeasur random parallel design develop site repres function group nation mass media promot use attract overweightobes australian adult base bodi mass index bmi calcul selfreport height weight elig respond n random alloc one function group informationbas n support n personalizedsupport n support site includ tool weight tracker meal planner social network platform personalizedsupport site includ meal planner offer recommend person use algorithm base user prefer certain food dietari activ inform constant across site base exist test week weight loss program total wellb diet andor intervent particip complet demograph includ selfreport weight behavior evalu questionnair onlin usag websit featur object record screen data collect procedur perform onlin facetofac contactresult across group attrit high around first week remain particip week retent higher support site compar informationbas site week p averag number day site use vari signific p higher support site sd personalizedsupport site sd relat informationbas site sd total particip provid valid final weight week followup intentiontotreat analys use multipl imput reveal statist signific differ weight loss site p averag particip lost se initi bodi weight se lose initi weight within support condit level use onlin weight tracker predict weight loss model estim p age model estim p initi bmi model estim p associ frequenc use weight trackerconclus relat static control inclus social network featur person meal plan recommend webbas weight loss program demonstr addit effect user weight loss retent featur howev increas averag number day user engag system user support websit greater use weight tracker tool associ greater weight loss

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