How an Online Intervention to Prevent Excessive Gestational Weight Gain Is Used and by Whom: A Randomized Controlled Process Evaluation

This article was published in August 2014 in the Journal of Medical Internet Research.

This article was written by: Margaret Demment, Meredith Graham, and Christine Olson

Here is the abstract for the research article:

Background: Online interventions have emerged as a popular strategy to promote healthy behaviors. Currently, there is little agreement about how to capture online intervention engagement. It is also uncertain who engages with weight-related online interventions and how engagement differs by demographic and weight characteristics.

Objective: The objectives of this study were to (1) characterize how pregnant women engaged with features of an online intervention to prevent excessive gestational weight gain, (2) identify demographic and weight status subgroups of women within the sample, and (3) examine differences in use of intervention features across the demographic and weight status subgroups.

Methods: A sample of racially and socioeconomically diverse pregnant women from a northeastern US city was assigned to the intervention group in a randomized controlled trial to prevent excessive gestational weight gain (n=1014). The intervention website included these features: weight-gain tracker, health-related articles, blogs, physical activity and diet goal-setting tools, and local resources. Engagement variables were created to capture the amount, consistency, and patterns of feature use across pregnancy using latent class analysis. Demographic/weight status subgroups were also created using latent class analysis. Differences in engagement across the demographic/weight status subgroups were examined using chi-square analysis.

Results: Six engagement patterns emerged: “super-users” (13.02%, 132/1014), “medium-users” (14.00%, 142/1014), “consistent weight-tracker users” (14.99%, 152/1014); “almost consistent weight-tracker users” (21.99%, 223/1014), “inconsistent weight-tracker users” (15.98%, 162/1014), and “non-users” (20.02%, 203/1014). Four demographic/weight status subgroups emerged: three minority and one white. There were different engagement patterns by demographic/weight status subgroups. Super-users were more likely to be in the white subgroup, while non-users were more likely to be in the minority subgroups. However, around a third of women in minority subgroups were consistently or almost consistently engaging with the weight-tracker (black, young women, 32.2%, 67/208; black, heavier women, 37.9%, 50/132; Hispanic women, 27.4%, 32/117).

Conclusions: While white and higher income women had higher engagement in general, depending on the measure, there was still considerable engagement by the minority and low-income women.

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