Features Predicting Weight Loss in Overweight or Obese Participants in a Web-Based Intervention: Randomized Trial

Abstract

BackgroundObesity 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.ObjectiveTo 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.MethodsWe 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.ResultsAcross 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.ConclusionsRelative to a static control, inclusion of social networking features and pers...

Social media interventions for diet and exercise behaviours: a systematic review and meta-analysis of randomised controlled trials

“…Sixteen studies intervened in an adult population13–15 19–21 23 24 26–28 30–34 and six in children and youth populations up to the age of 25. 16–18 22 25 29 Overweight or obesity were inclusion criteria in 13 studies 13 14 17 20 21 23 26–28 31–34. Six studies targeted women exclusively16 18 28 29 33 34 while only one solely targeted men 27…”

Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions

“…31 These can include the number of visits/uses, modules or features used, time spent on the intervention, number and type of pages visited, or response to alerts or reminders. 32 Usage metrics can provide valuable insights, but are typically large, complex data sets that are challenging to interpret. For example, additional qual-itative data can be needed to provide explanations for observed differences in usage metrics between partici-pants or intervention groups.…”

Characterizing Social Networks and Communication Channels in a Web-Based Peer Support Intervention

“…2,3 However, few studies offer more than one social networking channel, and our understanding of how participants engage with these types of social networking features is quite limited. For example, Brindal et al 3 provided an extensive social networking intervention for overweight adults, but measures of use for each channel were limited to the proportion of participants who used each component.…”

“…2,3 However, few studies offer more than one social networking channel, and our understanding of how participants engage with these types of social networking features is quite limited. For example, Brindal et al 3 provided an extensive social networking intervention for overweight adults, but measures of use for each channel were limited to the proportion of participants who used each component. The purposes of this study were to address existing gaps in the literature by characterizing social networking across four distinct channels of social communication and better understand how participant characteristics influence the type and nature of social networking within these types of interventions.…”